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 jitter 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.

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.

Design and Application of the Automatic Monitoring System for Wastewater Source in Shandong Province

In order to realize real-time online monitoring of the wastewater source enterprises,manage and issue monitoring information,this paper comprehensively uses automatic control,embedded data acquisition and transmission,distributed computing and data processing,geographic information system,etc.to develop automatic monitoring system of the wastewater source in Shandong Province.This system incorporates automatic monitoring information acquisition,transmission and daily work as an organic whole.The system realizes not only the continuous online monitoring of wastewater source enterprise,but also the deep excavation and utilization on monitoring information.It provides scientific and objective basis for energy saving,consumption reduction,carbon emission reduction,total amount control and other environmental management works,and meets the requirements of environmental management and related departments to wastewater source management.

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.

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.

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.

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.

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.

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.

Application of Intelligent Blood Temperature and Humidity Monitoring System in Blood Station

Objective: To explore the application of intelligent blood temperature and humidity monitoring system in cold chain management of blood station. Methods: Through the monitoring of fifty sets of cold-chain equipment in the central blood station in Hezhou for 6 months, the differences between the management of the automatic temperature and humidity monitoring system and the manual management were compared in terms of real-time recording, equipment alarm, data storage, historical data traceability and data analysis. Results: Temperature and humidity automatic monitoring system can automatically real-time acquisition, transmission, storage and alarm according to the required time interval;meanwhile, historical data can be quickly exported and traced, data and charts can be analyzed, and the alarm is real-time and effective. Conclusion: The system can effectively monitor the process of blood cold chain in blood stations and play a key role in ensuring blood quality. It can be popularized and used in blood stations.

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.

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%.

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.

Effects of Specimen Height on the Acoustic Emission Rate Value 'a' for Cement Mortar

In order to study the size effect on the AE rate ’a’ value,three kinds of mix ratios were set up by different particle sizes and water cement ratios,45 cement mortar specimens with five different heights were tested under axial compression.And the whole damage processes were monitored by full-digital acoustic emission acquisition system,followed by an analysis of mechanical behavior and AE activity.The experimental results show that the height of the cement specimen has significant effects on the compressive strength and the acoustic emission rate ’a’ value,but a slight effect on the accumulated AE hits number,which is analyzed from aspects of failure process of cement mortar specimens.

Performance of overlapped shield tunneling through an integrated physical model tests,numerical simulations and real-time field monitoring

In this work,deformations and internal forces of an existing tunnel subjected to a closely overlapped shield tunneling are monitored and analyzed using a series of physical model experiments and numerical simulations.Effects of different excavation sequences and speeds are explicitly considered in the analysis.The results of the physical model experiments show that the bottom-up tunneling procedure is better than the top-down tunneling procedure.The incurred deformations and internal forces of the existing tunnel increase with the excavation speed and the range of influence areas also increase accordingly.For construction process control,real-time monitoring of the power tunnel is used.The monitoring processes feature full automation,adjustable frequency,real-time monitor and dynamic feedback,which are used to guide the construction to achieve micro-disturbance control.In accordance with the situation of crossing construction,a numerical study on the performance of power tunnel is carried out.Construction control measures are given for the undercrossing construction,which helps to accomplish the desired result and meet protection requirements of the existing tunnel structure.Finally,monitoring data and numerical results are compared,and the displacement and joint fracture change models in the power tunnel subject to the overlapped shield tunnel construction are analyzed.

Construction risks of Huaying mount tunnel and countermeasures

The Chongqing-Guang＇an motorway is planned to cross Huaying mount at Jingguan town of Chongqing city. The whole mount is a colossal anticline whose core is consisted of coal measure strata （upper Permian Longtan formation P21） and the limbs are limestone strata （middle Triassic Leikoupo formation T21 and lower Triassic Jialingjiang formation T1j）. The tunneling is full of risks of collapse, gas explosion or gas outburst, water （mud） inrush, gas inrush because of existence of faults, high pressure gas, karst tectonics and coal goafs around the tunnel. In order to cope with the high risk, two main countermeasures were taken to ensure security of construction. One is geology prediction, and the other is automatic wireless real-time monitoring system, which contains monitoring of video, wind speed, poisonous gas （CH4, CO, H2S, SO2）, people location, and automatic power-off equipment while gas contents being more than warning threshold. These ascertained the engineering safety effectively.