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 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.

Design of Signal Lamp Filament Monitoring Alarm Instrument

To improve the reliability of the light emitting diode(LED)signal lamp filament current monitoring alarm instrument for metro systems,a new type of hot standby online monitoring apparatus was developed which is based on synchronous transmission data(STD)bus technology.In this system,a double hot standby mode can be achieved by adopting bus arbitration.In addition,to detect the effective value of alternating current which is from 0 to 200 mA in the signal lamp lighting circuit,a precision rectifier signal conditioning circuit and an isolated acquisition circuit were designed.This new type of alarm instrument has high detection accuracy and could meet the functional requirements for metro signal systems after comparing it with some industry products that were applied on the spot.

Distributed fiber optic sensors for tunnel monitoring:A state-of-the-art review

Distributed fiber optic sensors(DFOSs)possess the capability to measure strain and temperature variations over long distances,demonstrating outstanding potential for monitoring underground infrastructure.This study presents a state-of-the-art review of the DFOS applications for monitoring and assessing the deformation behavior of typical tunnel infrastructure,including bored tunnels,conventional tunnels,as well as immersed and cut-and-cover tunnels.DFOS systems based on Brillouin and Rayleigh scattering principles are both considered.When implementing DFOS monitoring,the fiber optic cable can be primarily installed along transverse and longitudinal directions to(1)measure distributed strains by continuously adhering the fiber to the structure’s surface or embedding it in the lining,or(2)measure point displacements by spot-anchoring it on the lining surface.There are four critical aspects of DFOS monitoring,including proper selection of the sensing fiber,selection of the measuring principle for the specific application,design of an effective sensor layout,and establishment of robust field sensor instrumentation.These four issues are comprehensively discussed,and practical suggestions are provided for the implementation of DFOS in tunnel infrastructure monitoring.

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.

Deformation warning index for reinforced concrete dam based on structural health monitoring data and numerical simulation

The material mechanical parameters of the dam body and foundation will change when a dam is reinforced during the aging process.This causes significant changes in the structural state of the project and makes it difficult to ensure its structural safety.In this study,a new deformation warning index for reinforced concrete dams was developed according to the prototype monitoring data,statistical models,three-dimensional finite element model(FEM)numerical simulation,and the critical conditions of the dam structure.A statistical model was established to separate the water pressure component.Then,a three-dimensional FEM of the reinforced concrete dam was constructed to simulate the water pressure component.Furthermore,the deformation components that affected the mechanical parameters of the dam under the same amount of reservoir water level change were separated and quantified accurately.In addition,the method for inversion of comprehensive mechanical parameters after dam reinforcement was used.The influence mechanisms of the deformation behavior of concrete dams under the reservoir water level and temperature changes were investigated.A new deformation warning index was developed by combining the forward-simulated critical water pressure component and temperature component in the period of extreme temperature decrease with the aging component separated by the statistical model.The new deformation warning index considers the structural state of the dam before and after reinforcement and links the structural strength criterion and the deformation evolution mechanisms.It provides a theoretical foundation and decision support for long-term service and operation management of reinforced dams.

Determination of Monitoring Control Value for Concrete Gravity Dam Spatial Deformation Based on POT Model

Deformation can directly reflect the working behavior of the dam,so determining the deformation monitoring control value can effectively monitor the safety of dam operation.The traditional dam deformation monitoring control value only considers the single measuring point.In order to overcome the limitation,this paper presents a new method to determine the monitoring control value for concrete gravity dam based on the deformations of multi-measuring points.A dam’s comprehensive deformation displacement is determined by the measured values at different measuring points on the positive inverted vertical line and the corresponding weight of eachmeasuring point.The projection pursuit method(PPM)combined with the grey wolf optimization(GWO)algorithm is used to determine the weight of each measuring point according to the spatial correlation distribution characteristics of dam deformation.The peaks over threshold(POT)model based on the extreme value theory is adopted to determine the monitoring control value with the obtained dam comprehensive deformation displacement.In addition,the POTmodel is improved with the automatic threshold determinationmethod based on the 3σcriterion in probability theory and the GWO algorithm,which can avoid subjectivity and randomness in determining the threshold.The results of the engineering application show the feasibility and applicability of the proposed method.

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.

Theory and Application of System Integration for Real-Time Monitoring of Core Rock-Fill Dam Filling Construction Quality

The theory and method of system integration for the real-time monitoring of core rock-fill dam filling con- struction quality are studied in this paper. First, the importance analysis of system integration factors is carried out with the analytic hierarchy process. Then, according to the analysis result of integration factors, the conceptual model of system integration is built based on function integration, index integration, technology integration and information integration, the index structure of core rock-fill dam filling construction quality control is constructed and the method of function integration and technology integration is studied. The mathematical model of process monitoring is built according to monitoring objective, process and indexes. Research results have been applied in Nuozhadu core rock-fill dam construction management, realizing system integration through building appropriate monitoring work flow and comprehensive information platform of digital dam.

Multi-dimensional database design and implementation of dam safety monitoring system

To improve the effectiveness of dam safety monitoring database systems, the development process of a multi-dimensional conceptual data model was analyzed and a logic design wasachieved in multi-dimensional database mode. The optimal data model was confirmed by identifying data objects, defining relations and reviewing entities. The conversion of relations among entities to external keys and entities and physical attributes to tables and fields was interpreted completely. On this basis, a multi-dimensional database that reflects the management and analysis of a dam safety monitoring system on monitoring data information has been established, for which factual tables and dimensional tables have been designed. Finally, based on service design and user interface design, the dam safety monitoring system has been developed with Delphi as the development tool. This development project shows that the multi-dimensional database can simplify the development process and minimize hidden dangers in the database structure design. It is superior to other dam safety monitoring system development models and can provide a new research direction for system developers.

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.

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.

Real-Time Grouting Monitoring and Visualization Analysis System for Dam Foundation Curtain Grouting

A real-time monitoring and 3D visualization analysis system is proposed for dam foundation curtain grouting. Based on the real-time control technology, the optimization method and the set theory, a mathematical model of the system is established. The real-time collection and transmission technology of the grouting data provides a data foundation for the system. The real-time grouting monitoring and dynamic alarming method helps the system control the grouting quality during the grouting process, thus, the abnormalities of grouting, such as jacking and hydraulic uplift, can be effectively controlled. In addition, the 3D grouting visualization analysis technology is proposed to establish the grouting information model(GIM). The GIM provides a platform to visualize and analyze the grouting process and results. The system has been applied to a hydraulic project of China as a case study, and the application results indicate that the real-time grouting monitoring and 3D visualization analysis for the grouting process can help engineers control the grouting quality more efficiently.

Genetic Regression Model for Dam Safety Monitoring

Under-fitting problems usually occur in regression models for dam safety monitoring.To overcome the local convergence of the regression, a genetic algorithm (GA) was proposed using a real parameter coding, a ranking selection operator, an arithmetical crossover operator and a uniform mutation operator, and calculated the least-square error of the observed and computed values as its fitness function. The elitist strategy was used to improve the speed of the convergence. After that, the modified genetic algorithm was applied to reassess the coefficients of the regression model and a genetic regression model was set up. As an example, a slotted gravity dam in the Northeast of China was introduced. The computational results show that the genetic regression model can solve the under-fitting problems perfectly.

Real-Time, Automatic and Wireless Bridge Monitoring System Based on MEMS Technology

Currently, the monitoring of bridges in China heavily relies on manual operation, which has several major problems. It generally takes a very long time to complete an inspection process on bridges. The manual data is sometimes unreliable or even wrong in the case of careless operation. The inspection activity itself is dangerous for inspectors, e.g., bridges are located in the sea or river. Some semi-automatic monitoring methods are recently employed, but they are either very expensive or do not work properly. Therefore, the traditional bridge monitoring process becomes an increasing challenge for bridge operators. In this paper, a real-time and automatic bridge monitoring system is presented to meet the bridge monitoring needs, and MEMS （Micro Electro Mechanical Systems） are the key building block in this system. By using the MEMS-based sensors, it is much more efficient and accurate in monitoring bridges with the measurement of inclination, acceleration, displacement, moisture, temperature, stress and other data.

Monitoring the Technical Condition of Dams of Hydroelectric Power Plants with an Automated Monitoring and Earthquake Registration System

An approach is described that has been developed for auxiliary monitoring of technical condition of hydropower plant dams. It is based on analysis of changes in dynamic characteristics of dams obtained by an automated monitoring and earthquake registration system that records microseismic vibrations of structures. The configuration of the system as well as the results of seismometric monitoring of the dam of Krasnoyarsk hydroelectric power plant are described. To study behavior of the dam under normal and extreme loads it was proposed to develop a model of the dam with the use of the finite element method.

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.

Monitoring models for base flow effect and daily variation of dam seepage elements considering time lag effect

Affected by external environmental factors and evolution of dam performance, dam seepage behavior shows nonlinear time-varying characteristics. In this study, to predict and evaluate the long-term development trend and short-term fluctuation of the dam seepage behavior, two monitoring models were developed, one for the base flow effect and one for daily variation of dam seepage elements. In the first model, to avoid the influence of the time lag effect on the evaluation of seepage variation with the time effect component of seepage elements, the base values of the seepage element and the reservoir water level were extracted using the wavelet multi-resolution analysis method, and the time effect component was separated by the established base flow effect monitoring model. For the development of the daily variation monitoring model for dam seepage elements, all the previous factors, of which the measured time series prior to the dam seepage element monitoring time may have certain influence on the monitored results, were considered. Those factors that were positively correlated with the analyzed seepage element were initially considered to be the support vector machine(SVM) model input factors, and then the SVM kernel function-based sensitivity analysis was performed to optimize the input factor set and establish the optimized daily variation SVM model. The efficiency and rationality of the two models were verified by case studies of the water level of two piezometric tubes buried under the slope of a concrete gravity dam.Sensitivity analysis of the optimized SVM model shows that the influences of the daily variation of the upstream reservoir water level and rainfall on the daily variation of piezometric tube water level are processes subject to normal distribution.