Design and Construction of Automatic Monitoring System for Open-pit Coal Mine Slopes

[Objectives]To monitor the stability of open-pit coal mine slopes in real time and ensure the safety of coal mine production.[Methods]The automatic monitoring system of coal mine slope was explored in depth,and the core functions of the system were designed comprehensively.According to the design function of the automatic monitoring system,the slope automatic monitoring system was constructed.Besides,in accordance with the actual situation of the slope,the monitoring frequency of slopes was set scientifically,and the key indicators such as rainfall,deep displacement and surface displacement of the slopes were monitored in an all-round and multi-angle way.[Results]During the monitoring period,the overall condition of the slope remained good,and no landslides or other geological disasters occurred.At the same time,the overall rainfall in the slope area remained low.In terms of monitoring data,the horizontal displacement and settlement of the slopes increased first and then tended to be stable.Specifically,the maximum horizontal displacement during the monitoring period was 22.74 mm,while the maximum settlement was 18.65 mm.[Conclusions]The automatic slope monitoring system has obtained remarkable achievements in practical application.It not only improves the accuracy and efficiency of slope stability monitoring,but also provides valuable reference experience for similar projects.

Automatic Monitoring System for 3-D Deformation of Crustal Fault Based on Laser and Machine Vision

An automatic monitoring method of the 3-D deformation is presented for crustal fault based on laser and machine vision.The laser source and screen are independently set up in the headwall and footwall,the collimated laser beam creates a circular spot on the screen,meanwhile,the industrial camera captures the tiny deformation of the crustal fault by monitoring the change of the spot position.This method significantly reduces the cost of equipment and labor,provides daily sampling to ensure high continuity of data.A prototype of the automatic monitoring system is developed,and a repeatability test indicates that the error of spot jtter can be minimized by consecutive samples.Meanwhile,the environmental correction model is determined to ensure that environmental changes do not disturb the system.Furthermore,the automatic monitoring system has been applied at the deformation monitoring station(KJX02)of China Beishan underground research laboratory,where continuous deformation monitoring is underway.

Design of Intelligent Window Automatic Monitoring System Based on Microcontroller Control

To ensure the safety of residents’lives and property by using automatic opening and closing of ordinary windows,this article designs an intelligent window automatic monitoring system.The article proposes a software and hardware design scheme for the system,which comprises a microcontroller control module,temperature and humidity detection module,harmful gas detection module,rainfall detection module,human thermal radiation induction module,Organic Light-Emitting Diode(OLED)display module,stepper motor drive module,Wi-Fi communication module,etc.Users use this system to monitor environmental data such as temperature,humidity,rainfall,harmful gas concentrations,and human health.Users can control the opening and closing of windows through manual,microcontroller,and mobile application(app)remote methods,providing users with a more convenient,comfortable,and safe living environment.

Prototyping low-cost automatic weather stations for natural disaster monitoring

Weather events put human lives at risk mostly when people might occupy areas susceptible to natural disasters.Deploying Professional Weather Stations(PWS)in vulnerable areas is key for monitoring weather with reliable measurements.However,such professional instrumentation is notably expensive while remote sensing from a number of stations is paramount.This imposes challenges on the large-scale weather station deployment for broad monitoring from large observation networks such as in Cemaden—The Brazilian National Center for Monitoring and Early Warning of Natural Disasters.In this context,in this paper,we propose a Low-Cost Automatic Weather Station(LCAWS)system developed from Commercial Off-The-Shelf(COTS)and open-source Internet of Things(IoT)technologies,which provides measurements as reliable as a reference PWS for natural disaster monitoring.When being automatic,LCAWS is a stand-alone photovoltaic system connected wirelessly to the Internet in order to provide real-time reliable end-to-end weather measurements.To achieve data reliability,we propose an intelligent sensor calibration method to correct measures.From a 30-day uninterrupted observation with sampling in minute resolution,we show that the calibrated LCAWS sensors have no statistically significant differences from the PWS measurements.As such,LCAWS has opened opportunities for reducing maintenance costs in Cemaden's observational network.

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.

Automatic Monitoring of Chilling Requirement for Sweet Cherry and Its Application in Protected Cultivation

For sweet cherry, the economic benefit of protected cultivation is much higher than that of open field cultivation. However, problems such as nonuniform germination, aberrant flower, abortion, bad fruit setting and delayed maturation appear frequently due to the inaccurate regulation of chilling requirement. In order to solve the problems, an automatic system for monitoring, selecting and recording of chilling requirement in protected cultivation of sweet cherry was designed and developed. The system, which consisted of temperature detection, effective chilling temperature selection, signal conversion and output and chilling accumulation display, had been continuously used in protected cultivation of sweet cherry for 3 years and good effects had been achieved. The system monitored chilling accumu- lation accurately and made it more reasonable for the time selection of greenhouse covering, thus avoiding risks of nonuniform germination, bad fruit setting and de- layed maturation, etc. Under the coordinate application of this system with other modern measuring and controlling techniques such as automatic detection and alarming of temperature and humidity, the environment in greenhouses was regulated precisely that the uniform fruits were obtained and the expected yield, quality and harvest time were achieved.

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.

GPS AUTOMATIC MONITORING SYSTEM FOR OUTSIDE DEFORMATION OF GEHEYAN DAM ON THE QINGJIANG RIVER

In this paper,the background of developing GPS Automatic Monitoring System for outside deformation of Geheyan Dam is described concisely.The framework,precision and features of the system are stated in detail.Finally,the prospective application of the system is introduced.

Automatic Monitoring Electronic Tongue with MEAs for Environmental Analysis

An automatic monitoring electronic tongue based on differential pulse stripping voltammetry (DPSV) was developed for heavy metals analysis.Simultaneous detections of trace Zn(Ⅱ),Cd(Ⅱ),Pb(Ⅱ),Cu(Ⅱ),Fe(Ⅲ) and Cr(Ⅲ) in water samples were performed with three electrochemical sensors.The sensor chip combined a silicon-based Hg-coated Au microelectrode array (MEA) as the working electrode on one side with an Ag/AgCl reference electrode and a Pt counter electrode on the other side.With a computer controlled multipotentiostat,pumps and valves,the electronic tongue realized in-situ real-time detection of the six metals mentioned above at parts-per-billion level without manual operation.

Real-time Monitoring of Subsea Gas Pipelines,Offshore Platforms,and Ship Inspection Scores Using an Automatic Identification System

The aim of this research is to develop an algorithm and application that can perform real-time monitoring of the safety operation of offshore platforms and subsea gas pipelines as well as determine the need for ship inspection using data obtained from automatic identification system(AIS).The research also focuses on the integration of shipping database,AIS data,and others to develop a prototype for designing a real-time monitoring system of offshore platforms and pipelines.A simple concept is used in the development of this prototype,which is achieved by using an overlaying map that outlines the coordinates of the offshore platform and subsea gas pipeline with the ship’s coordinates(longitude/latitude)as detected by AIS.Using such information,we can then build an early warning system(EWS)relayed through short message service(SMS),email,or other means when the ship enters the restricted and exclusion zone of platforms and pipelines.The ship inspection system is developed by combining several attributes.Then,decision analysis software is employed to prioritize the vessel’s four attributes,including ship age,ship type,classification,and flag state.Results show that the EWS can increase the safety level of offshore platforms and pipelines,as well as the efficient use of patrol boats in monitoring the safety of the facilities.Meanwhile,ship inspection enables the port to prioritize the ship to be inspected in accordance with the priority ranking inspection score.

Study on Factors Influencing the Quality of Automatic Monitoring Data of Pollution Sources and Technical Assurance Measures

Automatic monitoring data of pollution sources is an important basis for environmental supervision and management.At present,it is difficult to guarantee the quality of automatic monitoring data of pollution sources,and it is difficult to play the role of the monitoring data.In response to this problem,the factors influencing the quality of automatic monitoring data of pollution sources were analyzed in detail,and technical assurance measures for the quality of automatic monitoring data of pollution sources in Shandong Province were studied.Besides,the dynamic management and control idea of automatic monitoring of pollution sources was proposed,and specific technical measures were analyzed from five aspects of standardizing automatic monitoring equipment of pollution sources,improving the data collection and transmission system,establishing a mechanism for reporting operating status information of monitoring equipment,setting alarm rules and alarm processing procedures,and statistically analyzing the operating status of the equipment.Practice has proved that the dynamic management and control system can effectively ensure the quality of automatic monitoring data of pollution sources.

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.

New Progress in Dam Monitoring Automation

This article briefly compares the different characteristics of dam automatic monitoring and manual monitoring, introduces the advantages of the Automatic Monitoring and three modes for the development of Automatic Monitoring adopted by different countries, expounds the structural characteristics and products of every class/kind of automatic monitoring instruments for deformation, piezometry, leakage,stress-strain, and so on, points out the latest progress in capacity sensing series instruments in China. This paper discusses the three data collection pattems, Concentration, Distribution,and Assemble of automatic monitoring system and the characteristics of distribution system of dam safety automatic monitoring in different industrial control fields, gives a brief introduction to the Intelligent Distribution System of Dam Safety Monitoring (DAMS-IV), developed by Nanjing Automation Research Institute (NARI) which is suitable for different scales of projects and with excellent performances.

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%.

Monitoring technique for seepage line of tailings dam

An automatic monitoring technique of the seepage line, including the monitoring design, the automatic monitoring system and the backfill technique of the measuring probe of pore-water pressure, was used in a tailings dam, and a shallow refractive seismic method was investigated for obtaining the seepage line of those areas outside the monitoring zone. The results show that the automatic monitoring has the error within ±3% relative to piezometric tube method and improves monitoring efficiency greatly, and the shallow refractive seismic method has the error within ±10% but expands the area of monitoring. Both of them can be used for a daily measurement in monitoring the seepage line. The result of the automatic monitoring also shows that not only the design of the survey line and the backfill technique of the measuring probe of pore-water pressure are reasonable and economic but also the reliability and safety of the automatic monitoring system are better. Testing result by the shallow refractive seismic method in tailings reveals that the energy excited by hammering iron sheet-pole is strong enough and safe, and that the character of anti-jamming by the detectors with long tailcone is better.

Improved Smith prediction monitoring AGC system based on feedback-assisted iterative learning control

The performance of Smith prediction monitoring automatic gauge control(AGC) system is influenced by model mismatching greatly in strip rolling process. Aiming at this problem, a feedback-assisted iterative learning control strategy, which learned unknown modeling error by using previous control information repeatedly, was introduced into Smith prediction monitoring AGC system. Firstly, conventional Smith predictor and improved Smith predictor with PI-P controller were analyzed. Secondly, on the basis of establishing of feedback-assisted iterative learning control strategy for improved Smith predictor, process control signal update law and control error were deduced, then convergence condition of this strategy was put forward and proved. Finally, after modeling the automatic position control system, the PI-P Smith prediction monitoring AGC system with feedback-assisted iterative learning control was researched through simulation. Simulation results indicate that this system remains stable during model mismatching. The robustness and response of monitoring AGC is improved by development of feedback-assisted iterative learning control strategy for PI-P Smith predictor.

Investigation of monitoring system for high-speed railway subgrade frost heave

This paper presents methods for monitoring frost heave, device requirements, testing principals, and data analysis re- quirements, such as manual leveling observation, automatic monitoring （frost heave, frost depth, and moisture）, track dynamic detection, and track status detection. We focused on the requirements of subgrade frost heave monitoring for high speed railways, and the relationship of different monitoring methods during different phases of the railway. The com- prehensive monitoring system of high speed railway subgrade frost heave provided the technical support for dynamic design during construction and safe operation of the rail system.

RESEARCH ON AUTOMATIC FOG IDENTIFICATION TECHNOLOGY BY METEOROLOGICAL SATELLITE REMOTE SENSING

There is an urgent need for the development of a method that can undertake rapid, effective, and accurate monitoring and identification of fog by satellite remote sensing, since heavy fog can cause enormous disasters to China’s national economy and people's lives and property in the urban and coastal areas. In this paper, the correlative relationship between the reflectivity of land surface and clouds in different time phases is found, based on the analysis of the radiative and satellite-based spectral characteristics of fog. Through calculation and analyses of the relative variability of the reflectivity in the images, the threshold to identify quasi-fog areas is generated automatically. Furthermore, using the technique of quick image run-length encoding, and in combination with such practical methods as analyzing texture and shape fractures, smoothness, and template characteristics, the automatic identification of fog and fog-cloud separation using meteorological satellite remote sensing images are studied, with good results in application.