Application and Development of Protected Horticulture Intelligent Monitoring System

Protected horticulture makes use of related facilities, engineering technolo- gy and management technologies to create or improve local environment in order to provide optimal environment concerning controllable temperature, humidity, and light for farming and breeding industry, as well as product storage. Protected horticulture is independent to some extent, instead of relying greatly on nature, targeting full use of soil, climate and biological potential. The research concluded production characteristics of protected horticulture and analyzed the application of protected hor- ticulture intelligent monitoring system in protected greenhouse cultivation. In addition, the future development was proposed on protected horticulture intelligent monitoring system.

Hyperspectral Intelligent Monitoring System of Major Soil Nutrients Based on ArcGIS Engine

Based on the object-oriented concept,the hyperspectral intelligent monitoring system of major soil nutrients was designed and developed by using C# and ArcGIS Engine in combination with Microsoft SQL Server.The system mainly includes the following functions：file operation,basic map operation,spectral preprocessing,model management,nutrient content quick calculation,spatial distribution analysis,user management and so on.This system can accomplish the input and preprocessing of soil hyperspectra,and calculate the content of major nutrients,such as soil organic matter,nitrogen,phosphorus as well as potassium quickly and intelligently based on hyperspectral data.Thereby,the soil nutrients regional distribution in the research area can be analyzed by using the principle of geostatistics.This study can not only promote the practicability of soil quantitative remote sensing,but also provide references for the decision-making of agricultural fertilizing.

Data-driven intelligent monitoring system for key variables in wastewater treatment process

In wastewater treatment process(WWTP), the accurate and real-time monitoring values of key variables are crucial for the operational strategies. However, most of the existing methods have difficulty in obtaining the real-time values of some key variables in the process. In order to handle this issue, a data-driven intelligent monitoring system, using the soft sensor technique and data distribution service, is developed to monitor the concentrations of effluent total phosphorous(TP) and ammonia nitrogen(NH_4-N). In this intelligent monitoring system, a fuzzy neural network(FNN) is applied for designing the soft sensor model, and a principal component analysis(PCA) method is used to select the input variables of the soft sensor model. Moreover, data transfer software is exploited to insert the soft sensor technique to the supervisory control and data acquisition(SCADA) system. Finally, this proposed intelligent monitoring system is tested in several real plants to demonstrate the reliability and effectiveness of the monitoring performance.

Value Function Mechanism in WSNs-Based Mango Plantation Monitoring System

Mango fruit is one of the main fruit commodities that contributes to Taiwan’s income.The implementation of technology is an alternative to increasing the quality and quantity of mango plantation product productivity.In this study,a Wireless Sensor Networks(“WSNs”)-based intelligent mango plantation monitoring system will be developed that implements deep reinforcement learning(DRL)technology in carrying out prediction tasks based on three classifications:“optimal,”“sub-optimal,”or“not-optimal”conditions based on three parameters including humidity,temperature,and soil moisture.The key idea is how to provide a precise decision-making mechanism in the real-time monitoring system.A value function-based will be employed to perform DRL model called deep Q-network(DQN)which contributes in optimizing the future reward and performing the precise decision recommendation to the agent and system behavior.The WSNs experiment result indicates the system’s accuracy by capturing the real-time environment parameters is 98.39%.Meanwhile,the results of comparative accuracy model experiments of the proposed DQN,individual Q-learning,uniform coverage(UC),and NaÏe Bayes classifier(NBC)are 97.60%,95.30%,96.50%,and 92.30%,respectively.From the results of the comparative experiment,it can be seen that the proposed DQN used in the study has themost optimal accuracy.Testing with 22 test scenarios for“optimal,”“sub-optimal,”and“not-optimal”conditions was carried out to ensure the system runs well in the real-world data.The accuracy percentage which is generated from the real-world data reaches 95.45%.Fromthe resultsof the cost analysis,the systemcanprovide a low-cost systemcomparedtothe conventional system.

A Preliminary Study on Application Effect of Intelligent Insect Sexual Attraction Monitoring System

[Objectives]The paper was to verify and explore the application effect of intelligent insect sexual attraction monitoring system.[Methods]The data of Helicoverpa armigera and Spodoptera frugiperda monitored by intelligent insect sexual attraction monitoring system,manual survey and traditional pest monitoring tool were compared and analyzed,and the application effect of guiding field pest control was investigated.[Results]The statistical data of intelligent insect sexual attraction monitoring system were highly consistent with that of manual survey,and were consistent with that of traditional pest monitoring tool,which had good effect in guiding field control.[Conclusions]The monitoring data of intelligent insect sexual attraction monitoring system are accurate,efficient,real-time and practical.It can solve the problem of high monitoring intensity for the monitoring personnel and conform to the development direction of modern agriculture.

Sparsity-Assisted Intelligent Condition Monitoring Method for Aero-engine Main Shaft Bearing

Weak feature extraction is of great importance for condition monitoring and intelligent diagnosis of aeroengine.Aimed at achieving intelligent diagnosis of aero-engine main shaft bearing,an enhanced sparsity-assisted intelligent condition monitoring method is proposed in this paper.Through analyzing the weakness of convex sparse model,i.e.the tradeoff between noise reduction and feature reconstruction,this paper proposes an enhanced-sparsity nonconvex regularized convex model based on Moreau envelope to achieve weak feature extraction.Accordingly,a sparsity-assisted deep convolutional variational autoencoders network is proposed,which achieves the intelligent identification of fault state through training denoised normal data.Finally,the effectiveness of the proposed method is verified through aero-engine bearing run-to-failure experiment.The comparison results show that the proposed method is good at abnormal pattern recognition,showing a good potential for weak fault intelligent diagnosis of aero-engine main shaft bearings.

Self-Powered,Long-Durable,and Highly Selective Oil-Solid Triboelectric Nanogenerator for Energy Harvesting and Intelligent Monitoring

Triboelectric nanogenerators(TENGs)have potential to achieve energy harvesting and condition monitoring of oils,the“lifeblood”of industry.However,oil absorption on the solid surfaces is a great challenge for oil-solid TENG(O-TENG).Here,oleophobic/superamphiphobic O-TENGs are achieved via engineering of solid surface wetting properties.The designed O-TENG can generate an excellent electricity(with a charge density of 9.1μC m^(−2) and a power density of 1.23 mW m^(−2)),which is an order of magnitude higher than other O-TENGs made from polytetrafluoroethylene and polyimide.It also has a significant durability(30,000 cycles)and can power a digital thermometer for self-powered sensor applications.Further,a superhigh-sensitivity O-TENG monitoring system is successfully developed for real-time detecting particle/water contaminants in oils.The O-TENG can detect particle contaminants at least down to 0.01 wt%and water contaminants down to 100 ppm,which are much better than previous online monitoring methods(particle>0.1 wt%;water>1000 ppm).More interesting,the developed O-TENG can also distinguish water from other contaminants,which means the developed O-TENG has a highly water-selective performance.This work provides an ideal strategy for enhancing the output and durability of TENGs for oil-solid contact and opens new intelligent pathways for oil-solid energy harvesting and oil condition monitoring.

Design and application of electrical fire monitoring system in mining industry

To protect mining areas from electrical fire, it is very important to install electrical nre momtormg system to ensure safety in development of mineral resources and for buildings. In this paper, design for electrical fire monitoring and detection system with optional sensor modules has been proposed. In addition, necessity and suitability of electrical fire monitoring and detection system with optional sensor modules in mining areas have been reviewed. The designed electrical fire monitoring and detection system suit- able for work environment of mining industry is composed by host-computer monitoring software and slave-computer detectors. Monitoring detectors are manufactured by using embedded technology. Exter- nal shells deployed have superior enclosure performances and explosion-proof properties. It is easy to install and maintain the system. In general, the system has reached, or even exceeded standards specified in national standards for performances and appearances of such devices. Test results show application of electrical fire monitoring and detection system can effectively enhance monitoring intensity over the mining areas and provide reliable guarantee to ensure orderly development of mineral resources and to protect physical and property safety of citizens in these areas.

Progress of Intelligent Monitoring Technology for Wheat Fusarium Head Blight

Fusarium head blight is one of the most important diseases affecting wheat yield and quality.It is of great significance to carry out intelligent monitoring of wheat Fusarium head blight for high yield,high quality and sustainable development of wheat.On the basis of identifying the harms of wheat Fusarium head blight,this paper analyzed the monitoring technology of wheat Fusarium head blight based on satellite remote sensing,hyperspectral,near-infrared,Internet of things and photoelectric system,to provide a reference for the intelligent monitoring of wheat Fusarium head blight.

Research Progress and Prospect of Intelligent Tea Garden Technology Application

The development and application of internet plus modern tea industry technology is more and more extensive.As an important part of the development process of tea industry,intelligent tea garden plays an important role in the development of the whole industry.At present,intelligent tea garden technology is widely used in many fields such as intelligent monitoring,water and fertilizer integration,green prevention and control,quality and safety traceability.In this paper,the application of intelligent tea garden technology in tea gardens was reviewed.On this basis,the development trend of new information technology and tea industry was prospected,in order to provide some reference and thinking for the innovative research of new technology in tea garden in the future.

Tool wear condition monitoring method of five-axis machining center based on PSO-CNN

The effective monitoring of tool wear status in the milling process of a five-axis machining center is important for improving product quality and efficiency,so this paper proposes a CNN convolutional neural network model based on the optimization of PSO algorithm to monitor the tool wear status.Firstly,the cutting vibration signals and spindle current signals during the milling process of the five-axis machining center are collected using sensor technology,and the features related to the tool wear status are extracted in the time domain,frequency domain and time-frequency domain to form a feature sample matrix;secondly,the tool wear values corresponding to the above features are measured using an electron microscope and classified into three types:slight wear,normal wear and sharp wear to construct a target Finally,the tool wear sample data set is constructed by using multi-source information fusion technology and input to PSO-CNN model to complete the prediction of tool wear status.The results show that the proposed method can effectively predict the tool wear state with an accuracy of 98.27%;and compared with BP model,CNN model and SVM model,the accuracy indexes are improved by 9.48%,3.44%and 1.72%respectively,which indicates that the PSO-CNN model proposed in this paper has obvious advantages in the field of tool wear state identification.

The Intelligent Door Monitoring System on Internet

The hardware structure and software function of intelligence door monitoring control systems on Internet is expounded. The remote managing function, database function, and the realization of dialing users are introduced. The reset card is installed to improve reliability. The design of the system is reasonable and reliable. Results showed that 10 percent of the line investment have been cut off.

主井提升机载荷智能化监控系统应用探索

结合余吾煤业主井提升机实际情况,基于煤矿智能化建设评定管理办法,进行了提升机智能化监控系统整体设计,实现了钢丝绳在线监测、智能综合保护、远程无人操作等功能,提升了煤矿智能化水平,并详细给出了基于AI视频的钢丝绳张力及提升载荷监测系统设计方案。

矿山安全监控系统智能化现状及发展

随着科技水平的不断发展,矿山安全监控系统也变得越来越智能化、专业化,但当前矿山安全监控智能系统来说还存在一些不足。特别是安全监控系统中有伪数据的存在,会导致系统不能够很好的进行工作,在自我安装方面也会存在一定的制约。本文就《矿山安全监控系统升级改造技术方案》提出的相关要求,对矿山安全监控系统智能化的发展提出了几条对策,其中也包括如何更好的解决伪数据,使系统能够减少所受的干扰更好的进行自动识别和安装,也能够对矿山中的危险事故进行及时的报警。

浅谈煤矿智能通风与监测系统

以煤矿智能通风与监测系统为研究对象,分析了该系统运行的基本原理,明确了其重要性及存在的问题,并探讨了智能技术在煤矿领域中的应用,具体包括传感器技术、数据采集与传输、人工智能与大数据分析等。在此基础上,设计应用了智能通风与监测系统,其主要功能包括架构设计、数据处理、故障诊断和决策算法,最后探讨了该系统的可靠性与安全性,并对未来煤矿智能通风与监测技术的发展方向进行了分析,以期为其他煤矿的安全高效开采提供参考。

地面数字电视智能化监控系统的设计与实现

针对地面数字电视监控系统存在的系统功能缺失、界面显示单一、自动化程度低和融合拓展性差等问题,在充分运用智能化技术手段和设备设施的基础上,设计一套功能全面、智能化程度高的地面数字电视智能化监控系统,给出系统设计目标和软硬件实现方案,并围绕系统典型应用情况逐一分析该监控系统智能化解决故障的方式和呈现的实际效果。

The accuracy of arrhythmia detection and its clinical significance of intelligent wearable electrocardiogram monitoring devices in asymptomatic myocardial ischemia patients during daily activities.

This study focuses on the accuracy of arrhythmia detection by intelligent wearable electrocardiogram(ECG)monitoring devices in asymptomatic myocardial ischemia patients during daily activities.It elaborates on the technical principles,features,and working modes of such devices and makes a comparison with traditional ECG monitoring methods.Through a well-designed experimental approach involving data collection and analysis using specific evaluation metrics and standards,the accuracy of arrhythmia detection is evaluated.The relationship between arrhythmia and myocardial ischemia is explored,along with its impact on diagnosis,prognosis,and treatment strategy development.The application of these devices in daily activities,including feasibility,compliance,and analysis during different activity states and long-term trends,is also examined.Despite the potential benefits,technical limitations and barriers to clinical acceptance are identified,and future research directions are proposed.The findings contribute to a better understanding of the role and value of intelligent wearable ECG monitoring devices in the management of asymptomatic myocardial ischemia patients.

煤矿安全监控系统智能化水平的提升研究

为促进煤矿安全监控系统的自动化,智能化发展,基于煤矿智能化的主要概念,分析了当前安全监控系统在智能化发展方面现存的主要不足,从传输架构简单化方面、监控系统装备智能化、系统数据分析高效化三方面着手提出了提升煤矿安全监控系统智能化水平的思路,并对监控系统的部分功能进行完善及改进优化,实现了自主智能识别、分析、判断及控制等功能,促进了煤矿安全监控系统智能化水平的提升。

废气生物处理装置自动监控系统的设计

生物法处理是国内较新的一种高效工业有机废气污染治理技术,而具有自动监控功能的智能化处理装置尚处于研制开发阶段。介绍了一种基于生物法处理技术的废气处理装置的基本结构,提出了基于PLC及网络通信技术实现其自动监控系统的方案,详细介绍了该监控系统硬件和软件实现方法。试验结果表明,该系统不仅可实现废气处理过程中各参量测定,而且可按照设定的不同工艺对装置进行自动调节控制。具有功能完善、运行稳定可靠、性价比高等特点。

变电站监控系统智能化改造过渡方式研究

针对变电站监控系统智能化改造过程中遇到的站内信息缺失、防误闭锁逻辑不完整及主站无法监控全站设备等难题,研究采用状态信息代理装置来模拟智能终端发布信息,以保证全站监控联闭锁、调控信息等功能的完整。该方法可使监控系统智能化改造工作量小、停电时间短、易于安全掌控,实现新老监控系统的平稳过渡,对国内常规变电站的智能化改造具有一定的借鉴意义。