Development of an automatic monitoring system for rice light-trap pests based on machine vision

Monitring pest populations in paddy fields is important to effectively implement integrated pest management.Light traps are widely used to monitor field pests all over the world.Most conventional light traps still involve manual identification of target pests from lots of trapped insects,which is time-consuming,labor-intensive and error-prone,especially in pest peak periods.In this paper,we developed an automatic monitoring system for rice light-trap pests based on machine vision.This system is composed of an itelligent light trap,a computer or mobile phone client platform and a cloud server.The light trap firstly traps,kills and disperses insects,then collects images of trapped insects and sends each image to the cloud server.Five target pests in images are automatically identifed and counted by pest identification models loaded in the server.To avoid light-trap insects piling up,a vibration plate and a moving rotation conveyor belt are adopted to disperse these trapped insects.There was a close correlation(r=0.92)between our automatic and manual identification methods based on the daily pest number of one-year images from one light trap.Field experiments demonstrated the effectiveness and accuracy of our automatic light trap monitoring system.

Multilevel Pattern Mining Architecture for Automatic Network Monitoring in Heterogeneous Wireless Communication Networks

The rapid development of network technology and its evolution toward heterogeneous networks has increased the demand to support automatic monitoring and the management of heterogeneous wireless communication networks.This paper presents a multilevel pattern mining architecture to support automatic network management by discovering interesting patterns from telecom network monitoring data.This architecture leverages and combines existing frequent itemset discovery over data streams,association rule deduction,frequent sequential pattern mining,and frequent temporal pattern mining techniques while also making use of distributed processing platforms to achieve high-volume throughput.

Localized Coverage Connectivity Based on Shape and Area Using Mobile Sensor Robots in Wireless Sensor Networks

A wireless sensor network (WSN) is spatially distributing independent sensors to monitor physical and environmental characteristics such as temperature, sound, pressure and also provides different applications such as battlefield inspection and biological detection. The Constrained Motion and Sensor (CMS) Model represents the features and explain k-step reach ability testing to describe the states. The description and calculation based on CMS model does not solve the problem in mobile robots. The ADD framework based on monitoring radio measurements creates a threshold. But the methods are not effective in dynamic coverage of complex environment. In this paper, a Localized Coverage based on Shape and Area Detection (LCSAD) Framework is developed to increase the dynamic coverage using mobile robots. To facilitate the measurement in mobile robots, two algorithms are designed to identify the coverage area, (i.e.,) the area of a coverage hole or not. The two algorithms are Localized Geometric Voronoi Hexagon (LGVH) and Acquaintance Area Hexagon (AAH). LGVH senses all the shapes and it is simple to show all the boundary area nodes. AAH based algorithm simply takes directional information by locating the area of local and global convex points of coverage area. Both these algorithms are applied to WSN of random topologies. The simulation result shows that the proposed LCSAD framework attains minimal energy utilization, lesser waiting time, and also achieves higher scalability, throughput, delivery rate and 8% maximal coverage connectivity in sensor network compared to state-of-art works.

Communication Technology and Application of Seismic Precursor Network Instrument

In this paper, the communication technology of seismic precursor network instrument is introduced, including instruction format and returned information format of instrument login, status information acquisition, and current measured data acquisition. The remote monitoring alarm software is based on this technology, and also introduced that the structure of monitoring information table, abnormal alarm index, and monitoring strategy. The application of the software raises instrument running rate and observation data quality.

Convolutional neural network-based automatic image recognition for agricultural machinery

An internet of things-based subsoiling operation monitoring system for agricultural machinery is able to identify the type and operating state of a certain machinery by collecting and recognizing its images;however,it does not meet regulatory requirements due to a large image data volume,heavy workload by artificial selective examination,and low efficiency.In this study,a dataset containing machinery images of over 100 machines was established,which including subsoilers,rotary cultivators,reversible plows,subsoiling and soil-preparation machines,seeders,and non-machinery images.The images were annotated in tensorflow,a deep learning platform from Google.Then,a convolutional neural network(CNN)was designed for targeting actual regulatory demands and image characteristics,which was optimized by reducing overfitting and improving training efficiency.Model training results showed that the recognition rate of this machinery recognition network to the demonstration dataset reached 98.5%.In comparison,the recognition rates of LeNet and AlexNet under the same conditions were 81%and 98.8%,respectively.In terms of model recognition efficiency,it took AlexNet 60 h to complete training and 0.3 s to recognize 1 image,whereas the proposed machinery recognition network took only half that time to complete training and 0.1 s to recognize 1 image.To further verify the practicability of this model,6 types of images,with 200 images in each type,were randomly selected and used for testing;results indicated that the average recognition recall rate of various types of machinery images was 98.8%.In addition,the model was robust to illumination,environmental changes,and small-area occlusion,and thus was competent for intelligent image recognition of subsoiling operation monitoring systems.

Healthcare Monitoring Using Ensemble Classifiers in Fog Computing Framework

Nowadays,the cloud environment faces numerous issues like synchronizing information before the switch over the data migration.The requirement for a centralized internet of things(IoT)-based system has been restricted to some extent.Due to low scalability on security considerations,the cloud seems uninteresting.Since healthcare networks demand computer operations on large amounts of data,the sensitivity of device latency evolved among health networks is a challenging issue.In comparison to cloud domains,the new paradigms of fog computing give fresh alternatives by bringing resources closer to users by providing low latency and energy-efficient data processing solutions.Previous fog computing frameworks have various flaws,such as overvaluing response time or ignoring the accuracy of the result yet handling both at the same time compromises the network community.In this proposed work,Health Fog is integrated with the Optimized Cascaded Convolution Neural Network framework for diagnosing heart disease.Initially,the data is collected,and then pre-processing is done by Linear Discriminant Analysis.Then the features are extracted and optimized using Galactic Swarm Optimization.The optimized features are given into the Health Fog framework for diagnosing heart disease patients.It uses ensemble-based deep learning in edge computing devices,which automatically monitors real-life health networks such as heart disease analysis.Finally,the classifiers such as bagging,boosting,XGBoost,Multi-Layer Perceptron(MLP),and Partitions(PART)are used for classifying the data.Then the majority voting classifier predicts the result.This work uses FogBus architecture and evaluates the execution of power usage,bandwidth of the network,latency,execution time,and accuracy.

城市道路交通噪声监测技术与方法的现状与展望

日趋严重的道路交通噪声已成为城市的主要噪声源之一。精准高效地监测道路交通噪声是控制交通噪声污染的重要前提,然而噪声测量技术的发展脉络仍未被厘清。通过系统梳理2000—2021年国内外发表的282篇文献,发现当前道路交通噪声监测可分为静态多站点测量、自动监测网络、移动测量和参与式测量4种方法,并应向多源监测数据融合、提升数据时空分辨率、开发低成本且易集成的自动监测网络、建立公众参与式噪声测量规范与数据整合标准、制定以人为本的城市道路交通噪声动态监测体系等方向发展。未来可采取自动监测网络为主,常规静态测量、参与式测量和移动测量等方法为辅的技术体系监测道路交通噪声。该研究可为道路交通噪声监测技术革新及噪声污染防控提供参考。

深基坑自动化监测系统优化研究

通用版自动化监测系统难以满足基坑监测真实需求的现状,针对此问题,从监测基准网稳定性检校、改善通信链路、云平台数据展示等方面对通用版自动化监测系统进行优化改进,利用后验单位权方差Χ2检验方法对基准网稳定性进行检验,实时检校并更新监测基准网;采用基于LoRa通信的分布式监测站降低对基坑施工的干扰,并改善基坑内部信号传输环境;采用基坑方向位移、地下水位图示、开挖后累计变形等专业图表设计,提高云平台数据展示的专业性;并通过某建筑深基坑项目对优化内容进行应用验证,均取得了较好的应用效果,在保障监测数据准确性的同时,提高通用版自动化监测系统的行业针对性,为基坑自动化监测系统的建设发展提供了思路。

大坝安全监测数据异常识别模型簇研究

监测数据异常识别是大坝运行安全在线监测的前提和基础。单一识别方法难以实现高效准确识别,而RREW模型对规律不佳与单台阶数据序列容易漏判且计算效率低。为此,提出了基于卷积神经网络的一维VGG数据异常识别模型,建立了由统计回归、稳健回归、一维VGG识别模型等模型库和Pauta准则、MZ准则等判别准则库共同构成的大坝安全数据异常识别模型簇,并构建了不同数据类型与异常识别模型及预警准则的匹配机制。工程校验表明:一维VGG模型对不同序列长度、不同台阶占比的数据序列均具有较好的识别效果,能有效弥补传统回归模型和稳健回归模型的不足,由前述3种模型及两种准则共同构建的异常识别模型簇可实现海量数据异常的在线精准、快速识别,为大坝安全在线监测提供可靠的数据支持。

基于大数据的无线通信网络干扰信号自动告警方法

当前通信网络干扰信号自动告警规则一般设定为针对性的形式,自动告警的约束效果难以控制,导致可识别告警误差增加,为此提出对基于大数据的无线通信网络干扰信号自动告警方法的设计与验证研究。根据当前的测定需求及标准,先进行基础干扰信号告警特征提取,采用多阶的方式,加强对自动告警约束规则的控制效果,建立多阶定性告警限制规则,以此为基础,构建大数据网络干扰信号自动告警模型,采用PRI威胁信号识别完成自动告警处理。测试结果表明:对比于传统深度学习YOLOv2算法网络干扰信号自动告警测试组、传统相关系数网络干扰信号自动告警测试组,此次所设计的大数据网络干扰信号自动告警测试组最终得出的可识别告警误差被较好的控制在0.22以下,说明在大数据技术的辅助下,当前所设计的通信网络干扰信号自动告警方法实际应用效果更佳,针对性更强,误差可控,具有实际的应用价值。

住院病房中央监护系统的网络布局与维护维修

在分析住院病房中央监护系统的网络布局和功能结构基础上,探讨日常维护策略和常见故障维修方法。从功能结构上,住院病房中央监护系统分为网络架构、监护终端、中央监护和数据管理4个模块,实现住院患者生命体征监测和数据传输显示、分析报警的功能。当设备出现故障时,根据故障现象分析结构部件的出处,采用逐一排查的方式进行故障处理。在网络故障处理过程中,根据故障范围进行交换机前和机后的信号检测分析,当出现生理参数项监测结果异常时,需同时考虑人为操作、患者配合和设备软硬件故障等因素,根据观察和检测结果排查故障原因,设备故障均恢复正常使用。医护人员和设备工程师应熟悉中央监护系统的网络布局,开展日常维护,在故障维修中认真排查并规范操作,不断提升中央监护系统的运行质量。

基于障碍物和车位检测的单阶段多任务YOLO-Parking算法研究

文章提出一种基于YOLOv4的端到端多任务网络模型用于自动泊车系统中的感知任务,以环视图像(around view monitor, AVM)作为网络输入,基于卷积网络提取图像特征信息,通过YOLO和DMPR-PS(directional marking-point regression-parking slot)检测头实现停车位与障碍物并行检测。在PS 2.0公开数据集上进行验证的结果表明,所提出的多任务检测方法能够同时检测停车位和障碍物,障碍物识别平均精度均值达到89.72%,车位识别查准率达到93.53%,网络检测速率为34.0帧/s,在满足自动泊车感知任务需求的同时提升了系统的检测效率。该文研究成果对自动泊车感知技术的发展具有一定的意义。

离子膜电解装置智能管理系统应用总结

介绍了氯碱电解装置智能管理系统的组成、主要功能及运行效果.

Design of a System for Auto-patrolling Power Supplies and Displaying Beam Status In HIRFL

A practical application for patrolling the device’s status and displalying beam status of HIRFL has been described.With regard to the kinds of the controlled devices,the system consists of two parts:First,the alarm system is applied to monitor all power supplies,which are decomposed into six groups in accordance with their positions for the convenience of user’s operation.The amount of magnet lens and power supplies of

浅谈寺庙建筑电气消防设计

寺庙建筑基本是文物建筑,电气设计属于既有建筑改造,在保护文物的同时,消防电气设计是电气设计的重中之重。本文结合实际工程案例,从供配电设计、照明、火灾自动报警系统、电气火灾监控系统、消防应急广播、消防应急照明和疏散指示系统几个方面,探讨寺庙建筑的电气消防设计,以供参考。

注水复杂管网泄漏监测系统研究

注水复杂管网泄漏监测系统的研究目标是提高管网的运行效率,减少泄漏引起损失。研究主要涉及数据采集与处理、泄漏检测算法、人机交互综合管理等方面的工作。泄漏综合分析判断模型及管理平台研究,实现了2mm及以上孔径泄漏事件的有效判断监测。

多调整因子模糊控制的地下水水位自动监测方法

为提升地下水水位自动监测效果,研究一种基于多调整因子模糊控制的地下水水位自动监测方法。利用改进蝙蝠算法优化多调整因子,利用优化的多调整因子,设计多调整因子模糊控制器;将采集的地下水压力信号误差作为控制器的输入,输出压力传感器控制量,用于控制压力传感器的采集过程;在长短期记忆神经网络内输入采集的压力信号,输出地下水水位自动监测结果。实验证明:该方法可有效完成地下水水位自动监测工作,且监测误差较小;在不同降雨量时,该方法也能够有效监测地下水水位。

河南省地热资源监测技术研究

河南省地热资源丰富,而地热资源的可持续利用需要依赖准确的监测手段。布设地热监测网和监测井能够更好地探测和监测地下地热资源的分布和变化情况,而科学合理的监测频率不仅有助于节约成本,还可以有效提高数据的准确性。随着科学技术的发展,地热监测逐步趋向自动化,可以实现对地热动态数据的实时监测,从而制定科学的开发利用方案,避免对地热资源的过度开发和浪费。

基于人工智能算法的网络安全自动预警系统设计

随着网络技术的快速发展,网络安全问题日益突出。为了提高网络安全预警的准确性和实时性,提出一种基于人工智能算法的网络安全自动预警系统。该系统通过对网络流量和安全日志进行实时监测,利用人工智能算法进行数据分析和模式识别,及时发现潜在的安全威胁,并生成预警信息。实验结果表明,该系统能够有效提高网络安全预警的准确性和实时性,为网络安全防护提供有力支持。