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.

Real-time prediction of mechanical behaviors of underwater shield tunnel structure using machine learning method based on structural health monitoring data

Predicting the mechanical behaviors of structure and perceiving the anomalies in advance are essential to ensuring the safe operation of infrastructures in the long run.In addition to the incomplete consideration of influencing factors,the prediction time scale of existing studies is rough.Therefore,this study focuses on the development of a real-time prediction model by coupling the spatio-temporal correlation with external load through autoencoder network(ATENet)based on structural health monitoring(SHM)data.An autoencoder mechanism is performed to acquire the high-level representation of raw monitoring data at different spatial positions,and the recurrent neural network is applied to understanding the temporal correlation from the time series.Then,the obtained temporal-spatial information is coupled with dynamic loads through a fully connected layer to predict structural performance in next 12 h.As a case study,the proposed model is formulated on the SHM data collected from a representative underwater shield tunnel.The robustness study is carried out to verify the reliability and the prediction capability of the proposed model.Finally,the ATENet model is compared with some typical models,and the results indicate that it has the best performance.ATENet model is of great value to predict the realtime evolution trend of tunnel structure.

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.

Towards Cache-Assisted Hierarchical Detection for Real-Time Health Data Monitoring in IoHT

Real-time health data monitoring is pivotal for bolstering road services’safety,intelligence,and efficiency within the Internet of Health Things(IoHT)framework.Yet,delays in data retrieval can markedly hinder the efficacy of big data awareness detection systems.We advocate for a collaborative caching approach involving edge devices and cloud networks to combat this.This strategy is devised to streamline the data retrieval path,subsequently diminishing network strain.Crafting an adept cache processing scheme poses its own set of challenges,especially given the transient nature of monitoring data and the imperative for swift data transmission,intertwined with resource allocation tactics.This paper unveils a novel mobile healthcare solution that harnesses the power of our collaborative caching approach,facilitating nuanced health monitoring via edge devices.The system capitalizes on cloud computing for intricate health data analytics,especially in pinpointing health anomalies.Given the dynamic locational shifts and possible connection disruptions,we have architected a hierarchical detection system,particularly during crises.This system caches data efficiently and incorporates a detection utility to assess data freshness and potential lag in response times.Furthermore,we introduce the Cache-Assisted Real-Time Detection(CARD)model,crafted to optimize utility.Addressing the inherent complexity of the NP-hard CARD model,we have championed a greedy algorithm as a solution.Simulations reveal that our collaborative caching technique markedly elevates the Cache Hit Ratio(CHR)and data freshness,outshining its contemporaneous benchmark algorithms.The empirical results underscore the strength and efficiency of our innovative IoHT-based health monitoring solution.To encapsulate,this paper tackles the nuances of real-time health data monitoring in the IoHT landscape,presenting a joint edge-cloud caching strategy paired with a hierarchical detection system.Our methodology yields enhanced cache efficiency and data freshness.The corroborative numerical data accentuates the feasibility and relevance of our model,casting a beacon for the future trajectory of real-time health data monitoring systems.

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.

Pore-pressure and stress-coupled creep behavior in deep coal:Insights from real-time NMR analysis

Understanding the variations in microscopic pore-fracture structures(MPFS) during coal creep under pore pressure and stress coupling is crucial for coal mining and effective gas treatment. In this manuscript, a triaxial creep test on deep coal at various pore pressures using a test system that combines in-situ mechanical loading with real-time nuclear magnetic resonance(NMR) detection was conducted.Full-scale quantitative characterization, online real-time detection, and visualization of MPFS during coal creep influenced by pore pressure and stress coupling were performed using NMR and NMR imaging(NMRI) techniques. The results revealed that seepage pores and microfractures(SPM) undergo the most significant changes during coal creep, with creep failure gradually expanding from dense primary pore fractures. Pore pressure presence promotes MPFS development primarily by inhibiting SPM compression and encouraging adsorption pores(AP) to evolve into SPM. Coal enters the accelerated creep stage earlier at lower stress levels, resulting in more pronounced creep deformation. The connection between the micro and macro values was established, demonstrating that increased porosity at different pore pressures leads to a negative exponential decay of the viscosity coefficient. The Newton dashpot in the ideal viscoplastic body and the Burgers model was improved using NMR experimental results, and a creep model that considers pore pressure and stress coupling using variable-order fractional operators was developed. The model’s reasonableness was confirmed using creep experimental data. The damagestate adjustment factors ω and β were identified through a parameter sensitivity analysis to characterize the effect of pore pressure and stress coupling on the creep damage characteristics(size and degree of difficulty) of coal.

Semi-implantable device based on multiplexed microfilament electrode cluster for continuous monitoring of physiological ions

Modern medicine is increasingly interested in advanced sensors to detect and analyze biochemical indicators.Ion sensors based on potentiometric methods are a promising platform for monitoring physiological ions in biological subjects.Current semi-implantable devices are mainly based on single-parameter detection.Miniaturized semi-implantable electrodes for multiparameter sensing have more restrictions on the electrode size due to biocompatibility considerations,but reducing the electrode surface area could potentially limit electrode sensitivity.This study developed a semi-implantable device system comprising a multiplexed microfilament electrode cluster(MMEC)and a printed circuit board for real-time monitoring of intra-tissue K^(+),Ca^(2+),and Na^(+)concentrations.The electrode surface area was less important for the potentiometric sensing mechanism,suggesting the feasibility of using a tiny fiber-like electrode for potentiometric sensing.The MMEC device exhibited a broad linear response(K^(+):2–32 mmol/L;Ca^(2+):0.5–4 mmol/L;Na^(+):10–160 mmol/L),high sensitivity(about 20–45 mV/decade),temporal stability(>2weeks),and good selectivity(>80%)for the above ions.In vitro detection and in vivo subcutaneous and brain experiment results showed that the MMEC system exhibits good multi-ion monitoring performance in several complex environments.This work provides a platform for the continuous real-time monitoring of ion fluctuations in different situations and has implications for developing smart sensors to monitor human health.

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.

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%.

A wearable real-time telemonitoring electrocardiogram device compared with traditional Holter monitoring

Arrhythmias are very common in the healthy populations as well as patients with cardiovascular diseases.Among them,atrial fibrillation(AF)and malignant ventricular arrhythmias are usually associated with some clinical events.Early diagnosis of arrhythmias,particularly AF and ventricular arrhythmias,is very important for the treatment and prognosis of patients.Holter is a gold standard commonly recommended for noninvasive detection of paroxysmal arrhythmia.However,it has some shortcomings such as fixed detection timings,delayed report and inability of remote real-time detection.To deal with such problems,we designed and applied a new wearable 72-hour triple-lead H3-electrocardiogram(ECG)device with a remote cloud-based ECG platform and an expertsupporting system.In this study,31 patients were recruited and 24-hour synchronous ECG data by H3-ECG and Holter were recorded.In the H3-ECG group,ECG signals were transmitted using remote real-time modes,and confirmed reports were made by doctors in the remote expert-supporting system,while the traditional modes and detection systems were used in the Holter group.The results showed no significant differences between the two groups in 24-hour total heart rate(HR),averaged HR,maximum HR,minimum HR,premature atrial complexes(PACs)and premature ventricular complexes(PVCs)(P>0.05).The sensitivity and specificity of capture and remote automatic cardiac events detection of PACs,PVCs,and AF by H3-ECG were 93%and 99%,98%and 99%,94%and 98%,respectively.Therefore,the long-term limb triple-lead H3-ECG device can be utilized for domiciliary ECG self-monitoring and remote management of patients with common arrhythmia under medical supervision.

The Improvement of Earthquake Real-Time Monitoring System of Chinese National Digital Seismic Network

The earthquake real-time monitoring system of the Chinese National Digital Seismic Network has been in operation since"the Ninth Five-year Plan"period,and the stability of the system has been well tested.In recent years,with the continuous improvement of monitoring technology and increase of public demands,the original real-time monitoring system needs to be upgraded and improved in terms of timeliness,stability,accuracy and ease of operation.Therefore,by accessing a total of more than 1,000 seismic stations,reducing the seismic trigger threshold of the monitoring system,eliminating the false trigger stations and optimizing the seismic waveform display interface,the current earthquake monitoring demands can be satisfied on the basis of ensuring the stable operation of the system.

A real-time AI-assisted seismic monitoring system based on new nodal stations with 4G telemetry and its application in the Yangbi M_(S) 6.4 aftershock monitoring in southwest China

A rapidly deployable dense seismic monitoring system which is capable of transmitting acquired data in real time and analyzing data automatically is crucial in seismic hazard mitigation after a major earthquake.However,it is rather difficult for current seismic nodal stations to transmit data in real time for an extended period of time,and it usually takes a great amount of time to process the acquired data manually.To monitor earthquakes in real time flexibly,we develop a mobile integrated seismic monitoring system consisting of newly developed nodal units with 4G telemetry and a real-time AI-assisted automatic data processing workflow.The integrated system is convenient for deployment and has been successfully applied in monitoring the aftershocks of the Yangbi M_(S) 6.4 earthquake occurred on May 21,2021 in Yangbi County,Dali,Yunnan in southwest China.The acquired seismic data are transmitted almost in real time through the 4G cellular network,and then processed automat-ically for event detection,positioning,magnitude calculation and source mechanism inversion.From tens of seconds to a couple of minutes at most,the final seismic attributes can be presented remotely to the end users through the integrated system.From May 27 to June 17,the real-time system has detected and located 7905 aftershocks in the Yangbi area before the internal batteries exhausted,far more than the catalog provided by China Earthquake Networks Center using the regional permanent stations.The initial application of this inte-grated real-time monitoring system is promising,and we anticipate the advent of a new era for Real-time Intelligent Array Seismology(RIAS),for better monitoring and understanding the subsurface dynamic pro-cesses caused by Earth's internal forces as well as anthropogenic activities.

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.

Research and application of real-time monitoring and early warning thresholds for multi-temporal agricultural products information

Monitoring and early warning is an important means to effectively prevent risks in agricultural production,consumption and price.In particular,with the change of modes of national administration against the background of big data,improving the capacity to monitor agricultural products is of great significance for macroeconomic decision-making.Agricultural product information early warning thresholds are the core of agricultural product monitoring and early warning.How to appropriately determine the early warning thresholds of multi-temporal agricultural product information is a key question to realize real-time and dynamic monitoring and early warning.Based on the theory of abnormal fluctuation of agricultural product information and the research of substantive impact on the society,this paper comprehensively discussed the methods to determine the thresholds of agricultural product information fluctuation in different time dimensions.Based on the data of the National Bureau of Statistics of China(NBSC)and survey data,this paper used a variety of statistical methods to determine the early warning thresholds of the production,consumption and prices of agricultural products.Combined with Delphi expert judgment correction method,it finally determined the early warning thresholds of agricultural product information in multiple time,and carried out early warning analysis on the fluctuation of agricultural product monitoring information in 2018.The results show that:(1)the daily,weekly and monthly monitoring and early warning thresholds of agricultural products play an important early warning role in monitoring abnormal fluctuations with agricultural products;(2)the multitemporal monitoring and early warning thresholds of agricultural product information identified by the research institute can provide effective early warning on current abnormal fluctuation of agricultural product information,provide a benchmarking standard for China's agricultural production,consumption and price monitoring and early warning at the national macro level,and further improve the application of China's agricultural product monitoring and early warning.

Performance evaluation of near real-time condition monitoring in haul trucks

Strategic maintenance plays a key role in ensuring high availability and utilization of the haul trucks,and as equipment began to grow more complex towards the end of the 20th century,there was a need for a proactive maintenance strategy,which led to the development of condition-based maintenance.Realtime condition monitoring(RTCM)is the ability to perform condition monitoring in real-time and has the ability to alert maintenance and operations of abnormal conditions.These alarms can be used as an indication leading to a problem,and if a suitable corrective action is initiated in time,it could result in significant savings of equipment downtime and repair costs.This study aims to compare some maintenance performance indicators prior to and after implementation of RTCM strategy at a mine site using some tests of statistical significance.The study also indicated the presence of seasonality in the data,and thus the data was deseasonalized and detrended prior to being subjected to the statistical tests.Finally,the results indicated that RTCM strategy has proven to be successful in improving the availability for some of the failure categories chosen in this study.

Real-Time Monitoring of Climactic and Geotechnical Variables during Landslides on the Slopes of Serra do Mar and Serra da Mantiqueira (São Paulo State, Brazil)

The municipalities of Ubatuba, Campos do Jord?o, and S?o José dos Campos are located in the region of S?o Paulo State (Brazil). These municipalities are recognized nationally for having an elevated number of recorded landslides on slopes and embankments. In addition, these municipalities contain multiple areas that are at risk for landslides. Various soil landslides occurred in these municipalities in January 2013, when real-time climactic and geotechnical variables were monitored by automatic rain gauges, humidity sensors and soil temperature and suction devices. The resulting data were used to understand the functions of each variable in the occurrence of land- slides. Analyses of rainfall, humidity and soil temperature were used with field investigations to formulate a hypothesis regarding the predominant rupture mechanism and the role of each monitored variable in the deflagration of the soil landslides that occurred in the three studied municipalities. The geotechnical variable data revealed that both temperature and soil moisture contents played fundamental roles in the deflagration of shallow planar landslides in urban areas. The hourly rain intensity and/or rainfall accumulation for 24 and/or 72 h were responsible for the deflagration of the landslides that occurred in the studied areas, along with the existing anthropic constraints in the risk areas. Significant variations did not occur in the soil suction data during the landslides, principally due to the unsatisfactory sensor precision when reading field suction between –10 and?–100 kPA (±25%).

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.