Study on Rear-end Real-time Data Quality Control Method of Regional Automatic Weather Station

[Objective] The aim was to study the rear-end real-time data quality control method of regional automatic weather station. [Method] The basic content and steps of rear-end real-time data quality control of regional automatic weather station were introduced. Each element was treated with systematic quality control procedure. The existence of rear-end real time data of regional meteorological station in Guangxi was expounded. Combining with relevant elements and linear changes, improvement based on traditional quality control method was made. By dint of evaluation and relevant check of element, the quality of temperature and pressure was controlled. [Result] The method was optimized based on traditional quality control method, and it narrowed the effectiveness of real-time data quality control. The quality check of hourly precipitation applied relevant check of hourly minimum temperature, vertical consistency check of radar data, which can effectively improve the accuracy and credibility of hourly precipitation quality control. [Conclusion] The method was on trial for one year in the quality control of real-time data in the regional automatic meteorological station in Guangxi and had gained good outcome.

Real-Time, Automatic and Wireless Bridge Monitoring System Based on MEMS Technology

Currently, the monitoring of bridges in China heavily relies on manual operation, which has several major problems. It generally takes a very long time to complete an inspection process on bridges. The manual data is sometimes unreliable or even wrong in the case of careless operation. The inspection activity itself is dangerous for inspectors, e.g., bridges are located in the sea or river. Some semi-automatic monitoring methods are recently employed, but they are either very expensive or do not work properly. Therefore, the traditional bridge monitoring process becomes an increasing challenge for bridge operators. In this paper, a real-time and automatic bridge monitoring system is presented to meet the bridge monitoring needs, and MEMS （Micro Electro Mechanical Systems） are the key building block in this system. By using the MEMS-based sensors, it is much more efficient and accurate in monitoring bridges with the measurement of inclination, acceleration, displacement, moisture, temperature, stress and other data.

Quality Control System of Meteorological Real-time Data from Automatic Weather Stations in Shandong

The quality control system for meteorological real-time data from automatic weather stations in Shandong realized integration of communi- cation system and provincial quality control system, and an interaction platform which was mainly created by Web was set up. The system not only was fully guaranteed for the funning of basic business, also improved the reliability of the data.

Semi-supervised learning based probabilistic latent semantic analysis for automatic image annotation

In recent years,multimedia annotation problem has been attracting significant research attention in multimedia and computer vision areas,especially for automatic image annotation,whose purpose is to provide an efficient and effective searching environment for users to query their images more easily. In this paper,a semi-supervised learning based probabilistic latent semantic analysis( PLSA) model for automatic image annotation is presenred. Since it's often hard to obtain or create labeled images in large quantities while unlabeled ones are easier to collect,a transductive support vector machine( TSVM) is exploited to enhance the quality of the training image data. Then,different image features with different magnitudes will result in different performance for automatic image annotation. To this end,a Gaussian normalization method is utilized to normalize different features extracted from effective image regions segmented by the normalized cuts algorithm so as to reserve the intrinsic content of images as complete as possible. Finally,a PLSA model with asymmetric modalities is constructed based on the expectation maximization( EM) algorithm to predict a candidate set of annotations with confidence scores. Extensive experiments on the general-purpose Corel5k dataset demonstrate that the proposed model can significantly improve performance of traditional PLSA for the task of automatic image annotation.

Construction of multi-factor identification model for real-time monitoring and early warning of mine water inrush

As a new technical means that can detect abnormal signs of water inrush in advance and give an early warning,the automatic monitoring and early warning of water inrush in mines has been widely valued in recent years.Due to the many factors affecting water inrush and the complicated water inrush mechanism,many factors close to water inrush may have precursory abnormal changes.At present,the existing monitoring and early warning system mainly uses a few monitoring indicators such as groundwater level,water influx,and temperature,and performs water inrush early warning through the abnormal change of a single factor.However,there are relatively few multi-factor comprehensive early warning identification models.Based on the analysis of the abnormal changes of precursor factors in multiple water inrush cases,11 measurable and effective indicators including groundwater flow field,hydrochemical field and temperature field are proposed.Finally,taking Hengyuan coal mine as an example,6 indicators with long-term monitoring data sequences were selected to establish a single-index hierarchical early-warning recognition model,a multi-factor linear recognition model,and a comprehensive intelligent early-warning recognition model.The results show that the correct rate of early warning can reach 95.2%.

Real-time horizontality adjusting and control system of a large platform applied to satellite experiment

In order to satisfy a satellite horizontality requirement in an experiment, it is indispensable to monitor and adjust the horizontality of a large platform loading the satellite under the condition of ultra-low temperature with real time. So the control system design and control strategy are described in detail to accomplish the horizontality monitoring and adjusting. The system adopts the industry control computer as the upper computer and the SIEMENS S7-300 PLC as the lower computer. The upper computer that bases on industry configuration software IFIX takes charge of monitoring the platform and puts forward the control strategy. PLC takes charge of receiving the adjusting instructions and controlling the legs moving to accomplish the horizontality adjusting. The horizontality adjusting strategy is emphasized and the concept of grads is introduced to establish a mathematics model of the platform inclined state, so the adjusting method is obtained. Accordingly the key question of the automatic horizontality adjusting is solved in this control system.

Automatic Classification of Unstructured Blog Text

Automatic classification of blog entries is generally treated as a semi-supervised machine learning task, in which the blog entries are automatically assigned to one of a set of pre-defined classes based on the features extracted from their textual content. This paper attempts automatic classification of unstructured blog entries by following pre-processing steps like tokenization, stop-word elimination and stemming;statistical techniques for feature set extraction, and feature set enhancement using semantic resources followed by modeling using two alternative machine learning models—the na?ve Bayesian model and the artificial neural network model. Empirical evaluations indicate that this multi-step classification approach has resulted in good overall classification accuracy over unstructured blog text datasets with both machine learning model alternatives. However, the na?ve Bayesian classification model clearly out-performs the ANN based classification model when a smaller feature-set is available which is usually the case when a blog topic is recent and the number of training datasets available is restricted.

Weakly Supervised Abstractive Summarization with Enhancing Factual Consistency for Chinese Complaint Reports

A large variety of complaint reports reflect subjective information expressed by citizens.A key challenge of text summarization for complaint reports is to ensure the factual consistency of generated summary.Therefore,in this paper,a simple and weakly supervised framework considering factual consistency is proposed to generate a summary of city-based complaint reports without pre-labeled sentences/words.Furthermore,it considers the importance of entity in complaint reports to ensure factual consistency of summary.Experimental results on the customer review datasets(Yelp and Amazon)and complaint report dataset(complaint reports of Shenyang in China)show that the proposed framework outperforms state-of-the-art approaches in ROUGE scores and human evaluation.It unveils the effectiveness of our approach to helping in dealing with complaint reports.

Automatic detection of sow estrus using a lightweight real-time detector and thermal images

Determination of ovulation time is one of the most important tasks in sow reproduction management.Temperature variation in the vulva of the sows can be used as a predictor of ovulation time.However,the skin temperatures of sows in existing studies are obtained manually from infrared thermal images,posing an obstacle to the automatic prediction of ovulation time.In this study,an improved YOLO-V5s detector based on feature fusion and dilated convolution(FDYOLOV5s)was proposed for the automatic extraction of the vulva temperature of sows based on infrared thermal images.For the purpose of reducing the model complexity,the depthwise separable convolution and the modified lightweight ShuffleNet-V2 module were introduced in the backbone.Meanwhile,the feature fusion network structure of the model was simplified for efficiency,and a mixed dilated convolutional module was designed to obtain global features.The experimental results show that FD-YOLOV5s outperformed the other nine methods,with a mean average precision(mAP)of 99.1%,an average frame rate of 156.25 fps,and a model size of only 3.86 MB,indicating that the method effectively simplifies the model while ensuring detection accuracy.Using a linear regression between manual extraction and the results extracted using this method in randomly selected thermal images,the coefficients of determination for maximum and average vulvar temperatures reached 99.5%and 99.3%,respectively.The continuous vulva temperature of sows was obtained by the target detection algorithm,and the sow estrus detection was performed by the temperature trend and compared with the manually detected estrus results.The results showed that the sensitivity,specificity,and error rate of the estrus detection algorithm were 89.3%,94.5%,and 5.8%,respectively.The method achieves real-time and accurate extraction of sow vulva temperature and can be used for the automatic detection of sow estrus,which could be helpful for the automatic prediction of ovulation time.

贵州草海生态监管平台研究与实现

立足贵州草海湿地生态系统的特点和治理管理需求,通过创建地理信息大数据条件下的环境监管技术框架,建立统一的环境监管数据库,构建基于全样本分析的生态异常自动变化监测技术体系、基于深度学习的自动或半自动要素提取技术体系以及大数据统计分析技术体系,研发“一张云图”共享平台及人类区域活动监测平台,为贵州草海的全数字化监管提供了坚强的科技支撑,助力草海实现“水清、山绿、景美、人和、鸟欢”目标。

基于被动声呐音频信号的水中目标识别综述

基于被动声呐音频信号的水中目标识别是当前水下无人探测领域的重要技术难题,在军事和民用领域都应用广泛.本文从数据处理和识别方法两个层面系统阐述基于被动声呐信号进行水中目标识别的方法和流程.在数据处理方面,从基于被动声呐信号的水中目标识别基本流程、被动声呐音频信号分析的数理基础及其特征提取三个方面概述被动声呐信号处理的基本原理.在识别方法层面,全面分析基于机器学习算法的水中目标识别方法,并聚焦以深度学习算法为核心的水中目标识别研究.本文从有监督学习、无监督学习、自监督学习等多种学习范式对当前研究进展进行系统性的总结分析,并从算法的标签数据需求、鲁棒性、可扩展性与适应性等多个维度分析这些方法的优缺点.同时,还总结该领域中较为广泛使用的公开数据集,并分析公开数据集应具备的基本要素.最后,通过对水中目标识别过程的论述,总结目前基于被动声呐音频信号的水中目标自动识别算法存在的困难与挑战,并对该领域未来的发展方向进行展望.

岩土工程勘察信息化集成监管系统

随着现代科技的发展,岩土工程勘察信息化已经成为现代岩土工程领域的重要发展方向。笔者所讨论的岩土工程勘察集成监管系统,是基于信息化技术的勘察监管平台,旨在解决传统勘察过程中存在的数据不规范、信息不充分等问题(陈剑铭等,2023)。笔者阐述了岩土工程勘察信息化的现状及发展方向,通过对钻机状态监测装置、标准贯入试验(SPT)信息自动采集装置和圆锥动力触探试验(DPT)自动采集装置部件和功能的详细阐述说明.

基于云平台的城市轨道交通线网级列车监控系统设计

为解决传统ATS系统只能显示本线路的线路图、只能进行单一的线路行车调度指挥,以及物理硬件数量多、升级扩容难度大等问题,设计了一种基于云平台的城市轨道交通线网级列车监控系统(NATS)。该系统可以针对大型城市进行轨道交通全路网的信息显示,以便线网中心进行整体运营指挥和调度,提供行车指挥、运营指标分析、全局运行图编制、线网和线路协同指挥等运营管理功能,实现城市轨道交通监控调度集中化管理。借助于云平台,NATS系统能够虚拟化到云平台上,无需对系统本身进行大范围、深层次改动。利用云平台的特点,可以整合各线路的硬件资源,减少硬件设置,并根据NATS系统的规模灵活配置,便于既有线路数据迁移、后续线路接入和扩展,对系统整体性能有显著提升。本设计方案对各大城市的轨道交通线网级列车监控提供重要参考。

基于空间大数据的补充耕地合规性自动判别技术研究

为提升补充耕地项目监管效率和准确率,保证补充耕地数量准确、地类符合、区位合理,开展基于空间大数据的补充耕地合规性自动判别技术研究。本研究基于大数据框架、并行计算技术和GIS空间分析方法,设计了补充耕地合规性自动判别规则和指标体系,研制了补充耕地合规性内业自动判别的技术流程、算法和软件,开展了日常和专项补充耕地监管和核查,作为全国补充耕地项目监管的重要技术手段。经实际运行验证,项目平均分析时长2~4min,平均每月阻止5700余个问题项目入库。该研究能够为补充耕地项目立项、实施、验收提供合规性判别技术手段,提升补充耕地项目立项的合理性,提升补充耕地信息核实、监管、监督的技术水平,对确保我国耕地占补平衡制度的落实具有技术支撑作用。

基于实例分布约束的事件语义自动划分

针对离散分布于新闻文本集合中的事件语义难以聚合的问题,提出了基于实例分布约束的事件语义自动划分算法。首先,利用远程监督方法,构建用于事件语义划分的训练数据集;其次,设计基于实例分布约束的事件语义分类器,用于判断新的事件触发词的加入是否影响事件语义的聚合;最后,在该分类器的基础上设计事件语义集合生成算法,在不需要预先设定事件类型的情况下,将分布离散的事件触发词自动地划分到不同的事件语义集合中。结果表明本方法可有效实现事件语义的自动划分,为事件语义的高质量聚合提供了一种新的探索。

复杂真实环境下的调度电话转录算法研究

调度电话的应答是调度员日常工作的重要部分,也是事故回放审查的重要依据。为了提高事故分析及回放审查的效率,为调度指挥智能化打下基础,提出一种基于注意力机制及连接时序分类联合训练的调度电话转录算法,旨在提高复杂现实环境中调度电话的转录精确率及稳定性。通过在公开数据上添加调度大厅噪音来模拟现场环境,从而测试不同信噪比条件下算法的转录性能。在0 dB、1 dB、5 dB、10 dB的信噪比条件下测试转录错字率,结果分别是17.37%,16.48%,12.46%和9.69%,证明算法在强噪声环境下依旧能够保持稳定。算法包含半监督训练,实验表明,在小规模数据上,半监督训练相较于有监督训练可保证1%~2%的错字率降低。最后,算法在调度电话数据集上进行测试,并取得87.39%的关键字转录正确率。

面向煤矿集团的一体化综合管控平台研究

为确保煤矿集团公司层级对下属煤矿企业安全生产管控的及时性、精准性及有效性,促进煤矿企业安全高效生产,提出了一种面向煤矿集团的一体化综合管控平台。平台采用B/S架构设计了以接入层、基础设施层、数据资源层、业务支撑层及应用层为核心,安全运维管理和标准规范为保障体系的总体架构,详细阐述了业务子系统、可视化大屏子系统及移动应用APP三大功能模块设计及平台实现。实际应用结果表明,平台运行稳定、效果显著,可满足煤矿集团公司层级对下属煤矿企业安全生产监管和辅助决策需求,对促进煤矿集团公司数字化转型升级具有一定的推动作用。

全自动运行线路智能运控系统设计方案

[目的]随着城市轨道交通装备产业技术的快速发展和应用的不断成熟,城市轨道交通运营自动化程度不断提升。全自动运行系统因其具有高安全、高可靠、高效率的优势正被很多城市采用。全自动运行系统在提升运营安全与效率的同时,对行车调度员在融合分析与智能决策、应对复杂运营场景能力、扩展段场管理职责等方面提出了全新要求。为辅助行车调度员更快速、更高效地获取信息并作出决策,需对ATS(列车自动监控)系统进行智能化功能扩展。[方法]基于全自动运行线路安全智慧化运营、智能应急决策、智能应急处置等运营需求,提出了全自动运行线路智能运控系统。介绍了智能运控系统的方案构建,系统架构及系统外部接口,并对行车融合信息拓展、供电臂行车卡控、全自动运行图编辑、客流分析与行车评估、故障及应急指挥以及段场智能协同等功能模块的性能优化设计进行了深入分析。[结果及结论]深圳地铁16号线智能运控系统运用实践表明,对全自动运行线路ATS系统进行智能化功能扩展后,不仅提升了线路运营效率,更增强了全自动运行系统的安全性和可靠性,显著提升了运营管理智能化水平、系统对复杂运营环境的适应能力和对突发事件的快速反应能力。

构建以污染源自动监控为基础的非现场执法体系的建议

实施非现场执法是生态环境执法领域从“人海战术”转向“智慧执法”的必经之路,是现阶段非现场监管模式不断演化的必然方向。研究分析了污染源非现场监管向非现场执法转型过程中存在的自动监控设备管控边界不清、非现场执法顶层设计不完善、多维度电子数据证据体系未形成等问题,并对以污染源自动监控为基础的非现场执法体系构建提出如下建议:区分自动监控与自行监测的关系,明晰自动监控的管控边界;制定非现场执法实施方案等文件,完善顶层设计;建设全国非现场执法系统,实现统一的技术载体;完善非现场执法多维度电子数据证据体系,提升证据证明力。

基于卷积神经网络及遥感反演的生产建设项目识别方法及应用

由于遥感影像中的地物普遍存在异物同谱、同物异谱等现象,导致面向对象的多尺度和多阈值分割分类等解译方法无法在完全脱离人工干预的情况下使用,因而给解译带来成本高、效率较低等问题。本研究目的是建立一个实用、高效的生产建设项目扰动图斑自动识别模型,提高扰动图斑识别的效率和准确性。河南省作为水利部第一批遥感监管示范省,至今已积累大量生产建设项目水土保持空间数据。为此,笔者选择河南省作为研究区。提出一种基于神经网络及遥感反演模型的生产建设项目自动识别方法。该方法使用由5个卷积层和3个池化Pooling层的AlexNet对扰动图斑进行初步自动解译,并使用归一化植被指数、温度植被干旱指数等遥感反演模型进一步判断图斑是否为生产建设项目。笔者以成像时间为2020年、覆盖河南全省的G F-1、G F-6遥感影像为基础,使用生产建设项目扰动图斑的外包矩形裁剪上述影像获得训练样本,对样本进行标记,并将标记好的训练样本输入到AlexNet模型对其进行训练,完成训练后分别使用单一神经网络模型及神经网络模型+遥感反演模型进行两组测试。结果表明:1)本方法在降低误识别率和漏识别率的效果方面均比单纯卷积神经网络识别模型优异,误识别率平均降低23.88%,尤其在降低误识别率方面效果明显;2)本方法的计算时间,与卷积神经网络识别模型没有明显差异。综上,本研究成果可有效降低生产建设项目自动识别的误判率,有助于进一步提升生产建设项目水土保持遥感监管工作的自动化程度,在生产建设项目自动识别应用领域有一定的借鉴意义。