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.

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.

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.

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。

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.

面向高强极薄铜箔制造过程的多维度质量控制系统设计

高强极薄铜箔的制造过程具有工艺复杂、各阶段耦合性强、产品质量影响因素多等特点,面向制造过程的质量控制技术及相关质量控制系统将是实现其高效稳定生产的重要保证。针对现阶段铜箔制造企业在高强极薄铜箔制造工艺稳定性和质量控制方面相关技术和软件系统上的缺失,提出了一套多维度质量控制系统设计方案。通过对铜箔制造过程及其质量管控需求进行分析,建立了具有三层架构的系统框架,重点从产品质量控制、工艺质量智能保证及生产系统运行质量监测3个维度对各子系统的内涵及功能进行了设计,为实现高强极薄铜箔的高效稳定及智能化生产提供保证,并可供质量控制领域自主工业软件研发借鉴。

张家峁智能化煤矿巨系统关键技术研发与应用

为进一步加强传统采矿工程与新一代信息技术的不断深入融合发展,构建安全、智能、绿色、高效的煤矿,以张家峁煤矿为例介绍了智慧煤矿巨系统关键技术装备研发与示范矿井建设。以智慧煤矿总体规划及综合管理操作平台设计、智能化无人生产系统、智能化巷道掘进系统、环境感知及辅助系统、智能场区建设等为主线,进行张家峁智慧煤矿巨系统的建设。通过对煤矿5G移动通信技术,透明矿山三维管控平台关键技术,煤矿生产危险源分析预测技术,矿山采掘接续一张图集成技术,智能化无人工作面协同控制技术,环境感知及一通三防智能化技术等关键技术的应用解决了信息孤岛,提升了智能化采掘能力,提高了数据驱动决策,实现了矿区全域多源数据深度融合,生产效能、安全态势与业务流程协同优化的多维场景平衡综合分析决策,形成矿区智能化管理、高效协同运行新体系。

新时代智能化本科教学质量监控体系研究与探索

随着现代信息技术的发展,高等教育领域逐渐进入数字化、智能化和个性化时代。在提升本科教育教学质量方面,相对于传统本科教学质量监控体系,智能化本科教学质量监控体系覆盖面更大、效率更高、改进效果更好。传统本科教学质量监控体系面临着3项挑战,分别是教学质量监控队伍面对新时代高等教育的高质量发展要求仍存在差距、教学质量监控工作还存在一定误差、信息反馈的滞后直接影响持续改进效果等。高校进一步完善本科教学质量监控体系时,要坚持立德树人根本任务,切实保障人才培养质量;要完善本科教学质量标准,切实加强教学质量管理;要优化改进机制,切实提升教育教学成效;要打造质量文化,切实形成良好育人氛围。在推进智能化本科教学质量监控工作方面,高校可以从完善本科教学质量监控体系、校院两级智能化协同联动、教学质量监控机制、本科课程教学质量评价标准、数字化优质课程资源、数据挖掘系统、智慧教学空间、教学质量管理云平台等多方面开展积极探索。