This review explores glucose monitoring and management strategies,emphasizing the need for reliable and userfriendly wearable sensors that are the next generation of sensors for continuous glucose detection.In additio...This review explores glucose monitoring and management strategies,emphasizing the need for reliable and userfriendly wearable sensors that are the next generation of sensors for continuous glucose detection.In addition,examines key strategies for designing glucose sensors that are multi-functional,reliable,and cost-effective in a variety of contexts.The unique features of effective diabetes management technology are highlighted,with a focus on using nano/biosensor devices that can quickly and accurately detect glucose levels in the blood,improving patient treatment and control of potential diabetes-related infections.The potential of next-generation wearable and touch-sensitive nano biomedical sensor engineering designs for providing full control in assessing implantable,continuous glucose monitoring is also explored.The challenges of standardizing drug or insulin delivery doses,low-cost,real-time detection of increased blood sugar levels in diabetics,and early digital health awareness controls for the adverse effects of injectable medication are identified as unmet needs.Also,the market for biosensors is expected to expand significantly due to the rising need for portable diagnostic equipment and an ever-increasing diabetic population.The paper concludes by emphasizing the need for further research and development of glucose biosensors to meet the stringent requirements for sensitivity and specificity imposed by clinical diagnostics while being cost-effective,stable,and durable.展开更多
The residual subsidence caused by underground mining in mountain area has a long subsidence duration time and great potential harm,which seriously threatens the safety of people's production and life in the mining...The residual subsidence caused by underground mining in mountain area has a long subsidence duration time and great potential harm,which seriously threatens the safety of people's production and life in the mining area.Therefore,it is necessary to use appropriate monitoring methods and mathematical models to effectively monitor and predict the residual subsidence caused by underground mining.Compared with traditional level survey and InSAR(Interferometric Synthetic Aperture Radar)technology,GNSS(Global Navigation Satellite System)online monitoring technology has the advantages of long-term monitoring,high precision and more flexible monitoring methods.The empirical equation method of residual subsidence in mining subsidence is effectively combined with the rock creep equation,which can not only describe the residual subsidence process from the mechanism,but also predict the residual subsidence.Therefore,based on GNSS online monitoring technology,combined with the mining subsidence model of mountain area and adding the correlation coefficient of the compaction degree of caving broken rock and the Kelvin model of rock mechanics,this paper constructs the residual subsidence time series model of arbitrary point on the ground in mountain area.Through the example,the predicted results of the model in the inversion parameter phase and the dynamic prediction phase are compared with the measured data sequence.The results show that the model can carry out effective numerical calculation according to the GNSS monitoring data of any point on the ground,and the model prediction effect is good,which provides a new method for the prediction of residual subsidence in mountain mining.展开更多
[Objective]Real-time monitoring of cow ruminant behavior is of paramount importance for promptly obtaining relevant information about cow health and predicting cow diseases.Currently,various strategies have been propo...[Objective]Real-time monitoring of cow ruminant behavior is of paramount importance for promptly obtaining relevant information about cow health and predicting cow diseases.Currently,various strategies have been proposed for monitoring cow ruminant behavior,including video surveillance,sound recognition,and sensor monitoring methods.How‐ever,the application of edge device gives rise to the issue of inadequate real-time performance.To reduce the volume of data transmission and cloud computing workload while achieving real-time monitoring of dairy cow rumination behavior,a real-time monitoring method was proposed for cow ruminant behavior based on edge computing.[Methods]Autono‐mously designed edge devices were utilized to collect and process six-axis acceleration signals from cows in real-time.Based on these six-axis data,two distinct strategies,federated edge intelligence and split edge intelligence,were investigat‐ed for the real-time recognition of cow ruminant behavior.Focused on the real-time recognition method for cow ruminant behavior leveraging federated edge intelligence,the CA-MobileNet v3 network was proposed by enhancing the MobileNet v3 network with a collaborative attention mechanism.Additionally,a federated edge intelligence model was designed uti‐lizing the CA-MobileNet v3 network and the FedAvg federated aggregation algorithm.In the study on split edge intelli‐gence,a split edge intelligence model named MobileNet-LSTM was designed by integrating the MobileNet v3 network with a fusion collaborative attention mechanism and the Bi-LSTM network.[Results and Discussions]Through compara‐tive experiments with MobileNet v3 and MobileNet-LSTM,the federated edge intelligence model based on CA-Mo‐bileNet v3 achieved an average Precision rate,Recall rate,F1-Score,Specificity,and Accuracy of 97.1%,97.9%,97.5%,98.3%,and 98.2%,respectively,yielding the best recognition performance.[Conclusions]It is provided a real-time and effective method for monitoring cow ruminant behavior,and the proposed federated edge intelligence model can be ap‐plied in practical settings.展开更多
The continuous operation of On-Load Tap-Changers (OLTC) is essential for maintaining stable voltage levels in power transmission and distribution systems. Timely fault detection in OLTC is essential for preventing maj...The continuous operation of On-Load Tap-Changers (OLTC) is essential for maintaining stable voltage levels in power transmission and distribution systems. Timely fault detection in OLTC is essential for preventing major failures and ensuring the reliability of the electrical grid. This research paper proposes an innovative approach that combines voiceprint detection using MATLAB analysis for online fault monitoring of OLTC. By leveraging advanced signal processing techniques and machine learning algorithms in MATLAB, the proposed method accurately detects faults in OLTC, providing real-time monitoring and proactive maintenance strategies.展开更多
To optimize the self-organization network, self-adaptation, real-time monitoring, remote management capability, and equipment reuse level of the meteorological station supporting the portable groundwater circulation w...To optimize the self-organization network, self-adaptation, real-time monitoring, remote management capability, and equipment reuse level of the meteorological station supporting the portable groundwater circulation wells, and to provide real-time and effective technical services and environmental data support for groundwater remediation, a real-time monitoring system design of the meteorological station supporting the portable groundwater circulation wells based on the existing equipment is proposed. A variety of environmental element information is collected and transmitted to the embedded web server by the intelligent weather transmitter, and then processed by the algorithm and stored internally, displayed locally, and published on the web. The system monitoring algorithm and user interface are designed in the CNWSCADA development environment to realize real-time processing and analysis of environmental data and monitoring, control, management, and maintenance of the system status. The PLC-controlled photovoltaic power generating panels and lithium battery packs are in line with the concept of energy saving and emission reduction, and at the same time, as an emergency power supply to guarantee the safety of equipment and data when the utility power fails to meet the requirements. The experiment proves that the system has the characteristics of remote control, real-time interaction, simple station deployment, reliable operation, convenient maintenance, and green environment protection, which is conducive to improving the comprehensive utilization efficiency of various types of environmental information and providing reliable data support, theoretical basis and guidance suggestions for the research of groundwater remediation technology and its disciplines, and the research and development of the movable groundwater cycling well monitoring system.展开更多
Failures are very common during the online real-time monitoring of large quantities of complex liquids in industrial processes, and can result in excessive resource consumption and pollution. In this study, we introdu...Failures are very common during the online real-time monitoring of large quantities of complex liquids in industrial processes, and can result in excessive resource consumption and pollution. In this study, we introduce a monitoring method capable of non-contact original-state online real-time monitoring for strongly coated, high-salinity, and multi-component liquids. The principle of the method is to establish the relationship among the concentration of the target substance in the liquid （C）, the color space coor- dinates of the target substance at different concentrations （L＊, a＊, b＊）, and the maximum absorption wave- length （λmax）; subsequently, the optimum wavelength λT of the liquid is determined by a high-precision scanning-type monitoring system that is used to detect the instantaneous concentration of the target substance in the flowing liquid. Unlike traditional monitoring methods and existing online monitoring methods, the proposed method does not require any pretreatment of the samples （i.e., filtration, dilution, oxidation/reduction, addition of chromogenic agent, constant volume, etc.）, and it is capable of original- state online real-time monitoring. This method is employed at a large electrolytic manganese plant to monitor the Fe3. concentration in the colloidal process of the plant＇s aging liquid （where the concentra- tions of Fe3＋, Mn2＋, and （NH4）2SO4 are 0.5-18 mg.L 1, 35-39 g.L 1, and 90-110 g.L 1, respectively）. The relative error of this monitoring method compared with an off-line laboratory monitoring is less than 2%.展开更多
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 i...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.展开更多
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 eff...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.展开更多
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 triaxi...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.展开更多
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 bio...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.展开更多
Purpose–The purpose of this study is to address the deficiency in safety monitoring technology for 27.5 kV high-voltage cables within the railway traction power supply by analyzing the grounding methods employed in h...Purpose–The purpose of this study is to address the deficiency in safety monitoring technology for 27.5 kV high-voltage cables within the railway traction power supply by analyzing the grounding methods employed in high-speed railways and developing an effective monitoring solution.Design/methodology/approach–Through establishing a mathematical model of induced potential in the cable sheath and analyzing its influencing factors,the principle of grounding current monitoring is proposed.Furthermore,the accuracy of data collection and alarm function of the monitoring equipment were verified through laboratory simulation experiments.Finally,through practical application in the traction substation of the railway bureau on site,a large amount of data were collected to verify the stability and reliability of the monitoring system in actual environments.Findings–The experimental results show that the designed monitoring system can effectively monitor the grounding current of high-voltage cables and respond promptly to changes in cable insulation status.The system performs excellently in terms of data collection accuracy,real-time performance and reliability of alarm functions.In addition,the on-site trial results further confirm the accuracy and reliability of the monitoring system in practical applications,providing strong technical support for the safe operation of highspeed railway traction power supply systems.Originality/value–This study innovatively develops a 27.5kV high-voltage cable grounding current monitoring system,which provides a new technical means for evaluating the insulation status of cables by accurately measuring the grounding current.The design,experimental verification and application of this system in high-speed railway traction power supply systems have demonstrated significant academic value and practical significance,contributing innovative solutions to the field of railway power supply safety monitoring.展开更多
The monitoring of soil moisture content in paddy field is one of important parts and contents of regional soil moisture monitoring. But a good monitoring scheme hasn’t been established. A real-time monitoring scheme ...The monitoring of soil moisture content in paddy field is one of important parts and contents of regional soil moisture monitoring. But a good monitoring scheme hasn’t been established. A real-time monitoring scheme of soil moisture content in paddy field was put forward from two key links of soil moisture content monitoring and field water-layer monitoring. This scheme could meet the alternative monitoring requirements of soil moisture content in water layer and none-water layer. It had a good maneuverability and could provide references for practical work.展开更多
The assessment of the measurement error status of online Capacitor Voltage Transformers (CVT) within the power grid is of profound significance to the equitable trade of electric energy and the secure operation of the...The assessment of the measurement error status of online Capacitor Voltage Transformers (CVT) within the power grid is of profound significance to the equitable trade of electric energy and the secure operation of the power grid. This paper advances an online CVT error state evaluation method, anchored in the in-phase relationship and outlier detection. Initially, this method leverages the in-phase relationship to obviate the influence of primary side fluctuations in the grid on assessment accuracy. Subsequently, Principal Component Analysis (PCA) is employed to meticulously disentangle the error change information inherent in the CVT from the measured values and to compute statistics that delineate the error state. Finally, the Local Outlier Factor (LOF) is deployed to discern outliers in the statistics, with thresholds serving to appraise the CVT error state. Experimental results incontrovertibly demonstrate the efficacy of this method, showcasing its prowess in effecting online tracking of CVT error changes and conducting error state assessments. The discernible enhancements in reliability, accuracy, and sensitivity are manifest, with the assessment accuracy reaching an exemplary 0.01%.展开更多
In this research,an auxiliary illumination visual sensor system,an ultraviolet/visible(UVV)band visual sensor system(with a wavelength less than 780 nm),a spectrometer,and a photodiode are employed to capture insights...In this research,an auxiliary illumination visual sensor system,an ultraviolet/visible(UVV)band visual sensor system(with a wavelength less than 780 nm),a spectrometer,and a photodiode are employed to capture insights into the high-power disc laser welding process.The features of the visible optical light signal and the reflected laser light signal are extracted by decomposing the original signal captured by the photodiode via the wavelet packet decomposition(WPD)method.The captured signals of the spectrometer mainly have a wavelength of 400-900 nm,and are divided into 25 sub-bands to extract the spectrum features by statistical methods.The features of the plume and spatters are acquired by images captured by the UVV visual sensor system,and the features of the keyhole are extracted from images captured by the auxiliary illumination visual sensor system.Based on these real-time quantized features of the welding process,a deep belief network(DBN)is established to monitor the welding status.A genetic algorithm is applied to optimize the parameters of the proposed DBN model.The established DBN model shows higher accuracy and robustness in monitoring welding status in comparison with a traditional back-propagation neural network(BPNN)model.The effectiveness and generalization ability of the proposed DBN are validated by three additional experiments with different welding parameters.展开更多
Considering its structural features, geometric shapes, service mode, environmental media, mechanical behavior, etc, the special nature and complexity of tailings dam were summarized. The technical approach to safety m...Considering its structural features, geometric shapes, service mode, environmental media, mechanical behavior, etc, the special nature and complexity of tailings dam were summarized. The technical approach to safety management for tailings dam was proposed, which is the on-line automated monitoring and early warning information. The results show that a strong theoretical basis can be provided for security monitoring and security management of tailings dam. Online automated monitoring system for tailings dam has full implementation of the information. It is applied widely in Lingnan gold mine, Xiadian gold mine and Hedong gold mine in Zhaoyuan, Shandong Province, and achieves good effect.展开更多
Landslides have occurred frequently in the Luoshan mining area because of disordered mining.This paper discusses the landforms and physiognomy,hydro-meteorology,formation lithology,and geologic structure of the Luosha...Landslides have occurred frequently in the Luoshan mining area because of disordered mining.This paper discusses the landforms and physiognomy,hydro-meteorology,formation lithology,and geologic structure of the Luoshan mining area.It also describes the factors influencing the slope stability of landslide No.Ⅲ,determines the general parameters and typical section plane,analyzes the stress-strain state of the No.Ⅲ slope,and calculates its safety factors with FLAC3 D under saturated and natural conditions.Based on a stability analysis,a remote real-time monitoring system was applied to the No.Ⅲ slope,and these monitoring data were collected and analyzed.展开更多
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.D...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%.展开更多
Based on digital image processing technique, a real-time system is developed to monitor and detect the dynamic displacement of engineering structures. By processing pictures with a self-programmed software, the real-t...Based on digital image processing technique, a real-time system is developed to monitor and detect the dynamic displacement of engineering structures. By processing pictures with a self-programmed software, the real-time coordinate of an object in a certain coordinate system can be obtained, and further dynamic displacement data and curve of the object can also be achieved. That is, automatic gathering and real-time processing of data can be carried out by this system simultaneously. For this system, first, an untouched monitoring technique is adopted, which can monitor or detect objects several to hundreds of meters apart; second, it has flexible installation condition and good monitoring precision of sub-millimeter degree; third, it is fit for dynamic, quasi-dynamic and static monitoring of large engineering structures. Through several tests and applications in large bridges, good reliability and dominance of the system is proved.展开更多
A method based on solution of the inverse heat conduction problem was presented for online stress monitoring and fatigue life analysis of boiler drums. The mathematical model of the drum temperature distribution is ba...A method based on solution of the inverse heat conduction problem was presented for online stress monitoring and fatigue life analysis of boiler drums. The mathematical model of the drum temperature distribution is based on the assumptions that the difference of temperature along the longitudinal axis of the boiler drum is negligible with changes only in the radial direction and the circumferential direction, and that the outer surface of drum is thermaUy insulated. Combining this model with the control-volume method provides temperatures at different points on a cross-section of the drum. With the temperature data, the stresses and the life expectancy of the boiler drum are derived according to the ASME code. Applying this method to the cold start-up process of a 300 MW boiler demonstrated the absence of errors caused by the boundary condition assumptions on the inner surface of the drum and testified that the method is an applicable technique for the online stress monitoring and fatigue life analysis of boiler drums.展开更多
The online-monitoring methods for insulation performance of current transformers of 330-750 kV substation are analyzed and compared.The effectiveness and availability of each method are discussed.Main features,advanta...The online-monitoring methods for insulation performance of current transformers of 330-750 kV substation are analyzed and compared.The effectiveness and availability of each method are discussed.Main features,advantages and disadvantages of each method and its corresponding standard are also described.展开更多
基金supported by the National Research Foundation of Korea (NRF) grant funded by the Korean Government (MSIT) (No.2022M3J7A1062940,2021R1A5A6002853,and 2021R1A2C3011585)supported by the Technology Innovation Program (20015577)funded by the Ministry of Trade,Industry&Energy (MOTIE,Korea)。
文摘This review explores glucose monitoring and management strategies,emphasizing the need for reliable and userfriendly wearable sensors that are the next generation of sensors for continuous glucose detection.In addition,examines key strategies for designing glucose sensors that are multi-functional,reliable,and cost-effective in a variety of contexts.The unique features of effective diabetes management technology are highlighted,with a focus on using nano/biosensor devices that can quickly and accurately detect glucose levels in the blood,improving patient treatment and control of potential diabetes-related infections.The potential of next-generation wearable and touch-sensitive nano biomedical sensor engineering designs for providing full control in assessing implantable,continuous glucose monitoring is also explored.The challenges of standardizing drug or insulin delivery doses,low-cost,real-time detection of increased blood sugar levels in diabetics,and early digital health awareness controls for the adverse effects of injectable medication are identified as unmet needs.Also,the market for biosensors is expected to expand significantly due to the rising need for portable diagnostic equipment and an ever-increasing diabetic population.The paper concludes by emphasizing the need for further research and development of glucose biosensors to meet the stringent requirements for sensitivity and specificity imposed by clinical diagnostics while being cost-effective,stable,and durable.
基金supported by the Natural Science Foundation of Shanxi Province,China(202203021211153)National Natural Science Foundation of China(51704205).
文摘The residual subsidence caused by underground mining in mountain area has a long subsidence duration time and great potential harm,which seriously threatens the safety of people's production and life in the mining area.Therefore,it is necessary to use appropriate monitoring methods and mathematical models to effectively monitor and predict the residual subsidence caused by underground mining.Compared with traditional level survey and InSAR(Interferometric Synthetic Aperture Radar)technology,GNSS(Global Navigation Satellite System)online monitoring technology has the advantages of long-term monitoring,high precision and more flexible monitoring methods.The empirical equation method of residual subsidence in mining subsidence is effectively combined with the rock creep equation,which can not only describe the residual subsidence process from the mechanism,but also predict the residual subsidence.Therefore,based on GNSS online monitoring technology,combined with the mining subsidence model of mountain area and adding the correlation coefficient of the compaction degree of caving broken rock and the Kelvin model of rock mechanics,this paper constructs the residual subsidence time series model of arbitrary point on the ground in mountain area.Through the example,the predicted results of the model in the inversion parameter phase and the dynamic prediction phase are compared with the measured data sequence.The results show that the model can carry out effective numerical calculation according to the GNSS monitoring data of any point on the ground,and the model prediction effect is good,which provides a new method for the prediction of residual subsidence in mountain mining.
文摘[Objective]Real-time monitoring of cow ruminant behavior is of paramount importance for promptly obtaining relevant information about cow health and predicting cow diseases.Currently,various strategies have been proposed for monitoring cow ruminant behavior,including video surveillance,sound recognition,and sensor monitoring methods.How‐ever,the application of edge device gives rise to the issue of inadequate real-time performance.To reduce the volume of data transmission and cloud computing workload while achieving real-time monitoring of dairy cow rumination behavior,a real-time monitoring method was proposed for cow ruminant behavior based on edge computing.[Methods]Autono‐mously designed edge devices were utilized to collect and process six-axis acceleration signals from cows in real-time.Based on these six-axis data,two distinct strategies,federated edge intelligence and split edge intelligence,were investigat‐ed for the real-time recognition of cow ruminant behavior.Focused on the real-time recognition method for cow ruminant behavior leveraging federated edge intelligence,the CA-MobileNet v3 network was proposed by enhancing the MobileNet v3 network with a collaborative attention mechanism.Additionally,a federated edge intelligence model was designed uti‐lizing the CA-MobileNet v3 network and the FedAvg federated aggregation algorithm.In the study on split edge intelli‐gence,a split edge intelligence model named MobileNet-LSTM was designed by integrating the MobileNet v3 network with a fusion collaborative attention mechanism and the Bi-LSTM network.[Results and Discussions]Through compara‐tive experiments with MobileNet v3 and MobileNet-LSTM,the federated edge intelligence model based on CA-Mo‐bileNet v3 achieved an average Precision rate,Recall rate,F1-Score,Specificity,and Accuracy of 97.1%,97.9%,97.5%,98.3%,and 98.2%,respectively,yielding the best recognition performance.[Conclusions]It is provided a real-time and effective method for monitoring cow ruminant behavior,and the proposed federated edge intelligence model can be ap‐plied in practical settings.
文摘The continuous operation of On-Load Tap-Changers (OLTC) is essential for maintaining stable voltage levels in power transmission and distribution systems. Timely fault detection in OLTC is essential for preventing major failures and ensuring the reliability of the electrical grid. This research paper proposes an innovative approach that combines voiceprint detection using MATLAB analysis for online fault monitoring of OLTC. By leveraging advanced signal processing techniques and machine learning algorithms in MATLAB, the proposed method accurately detects faults in OLTC, providing real-time monitoring and proactive maintenance strategies.
文摘To optimize the self-organization network, self-adaptation, real-time monitoring, remote management capability, and equipment reuse level of the meteorological station supporting the portable groundwater circulation wells, and to provide real-time and effective technical services and environmental data support for groundwater remediation, a real-time monitoring system design of the meteorological station supporting the portable groundwater circulation wells based on the existing equipment is proposed. A variety of environmental element information is collected and transmitted to the embedded web server by the intelligent weather transmitter, and then processed by the algorithm and stored internally, displayed locally, and published on the web. The system monitoring algorithm and user interface are designed in the CNWSCADA development environment to realize real-time processing and analysis of environmental data and monitoring, control, management, and maintenance of the system status. The PLC-controlled photovoltaic power generating panels and lithium battery packs are in line with the concept of energy saving and emission reduction, and at the same time, as an emergency power supply to guarantee the safety of equipment and data when the utility power fails to meet the requirements. The experiment proves that the system has the characteristics of remote control, real-time interaction, simple station deployment, reliable operation, convenient maintenance, and green environment protection, which is conducive to improving the comprehensive utilization efficiency of various types of environmental information and providing reliable data support, theoretical basis and guidance suggestions for the research of groundwater remediation technology and its disciplines, and the research and development of the movable groundwater cycling well monitoring system.
文摘Failures are very common during the online real-time monitoring of large quantities of complex liquids in industrial processes, and can result in excessive resource consumption and pollution. In this study, we introduce a monitoring method capable of non-contact original-state online real-time monitoring for strongly coated, high-salinity, and multi-component liquids. The principle of the method is to establish the relationship among the concentration of the target substance in the liquid （C）, the color space coor- dinates of the target substance at different concentrations （L＊, a＊, b＊）, and the maximum absorption wave- length （λmax）; subsequently, the optimum wavelength λT of the liquid is determined by a high-precision scanning-type monitoring system that is used to detect the instantaneous concentration of the target substance in the flowing liquid. Unlike traditional monitoring methods and existing online monitoring methods, the proposed method does not require any pretreatment of the samples （i.e., filtration, dilution, oxidation/reduction, addition of chromogenic agent, constant volume, etc.）, and it is capable of original- state online real-time monitoring. This method is employed at a large electrolytic manganese plant to monitor the Fe3. concentration in the colloidal process of the plant＇s aging liquid （where the concentra- tions of Fe3＋, Mn2＋, and （NH4）2SO4 are 0.5-18 mg.L 1, 35-39 g.L 1, and 90-110 g.L 1, respectively）. The relative error of this monitoring method compared with an off-line laboratory monitoring is less than 2%.
基金This work is supported by the National Natural Science Foundation of China(Grant No.51991392)Key Deployment Projects of Chinese Academy of Sciences(Grant No.ZDRW-ZS-2021-3-3)the Second Tibetan Plateau Scientific Expedition and Research Program(STEP)(Grant No.2019QZKK0904).
文摘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.
基金supported by National Natural Science Foundation of China(NSFC)under Grant Number T2350710232.
文摘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.
基金supported by the National Natural Science Foundation of China(Nos.52121003,51827901 and 52204110)China Postdoctoral Science Foundation(No.2022M722346)+1 种基金the 111 Project(No.B14006)the Yueqi Outstanding Scholar Program of CUMTB(No.2017A03).
文摘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.
基金The authors would like to acknowledge financial support from the National Key R&D Program of China(Nos.2021YFF1200700 and 2021YFA0911100)the National Natural Science Foundation of China(Nos.T2225010,32171399,and 32171456)+4 种基金the Fundamental Research Funds for the Central Universities,Sun Yat-Sen University(No.22dfx02)Pazhou Lab,Guangzhou(No.PZL2021KF0003)The authors also would like to thank the funding support from the Opening Project of Key Laboratory of Microelectronic Devices&Integrated Technology,Institute of Microelectronics,Chinese Academy of Sciences,and State Key Laboratory of Precision Measuring Technology and Instruments(No.pilab2211)QQOY would like to thank the China Postdoctoral Science Foundation(No.2022M713645)JL would like to thank the National Natural Science Foundation of China(No.62105380)and the China Postdoctoral Science Foundation(No.2021M693686).
文摘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.
基金the China Railway Wuhan Bureau Group Co.,Ltd.under the 2023 Science and Technology Research and Development Plan(Second Batch)(Wuhan Railway Science and Information Letter[2023]No.269),classification code 23GD07.
文摘Purpose–The purpose of this study is to address the deficiency in safety monitoring technology for 27.5 kV high-voltage cables within the railway traction power supply by analyzing the grounding methods employed in high-speed railways and developing an effective monitoring solution.Design/methodology/approach–Through establishing a mathematical model of induced potential in the cable sheath and analyzing its influencing factors,the principle of grounding current monitoring is proposed.Furthermore,the accuracy of data collection and alarm function of the monitoring equipment were verified through laboratory simulation experiments.Finally,through practical application in the traction substation of the railway bureau on site,a large amount of data were collected to verify the stability and reliability of the monitoring system in actual environments.Findings–The experimental results show that the designed monitoring system can effectively monitor the grounding current of high-voltage cables and respond promptly to changes in cable insulation status.The system performs excellently in terms of data collection accuracy,real-time performance and reliability of alarm functions.In addition,the on-site trial results further confirm the accuracy and reliability of the monitoring system in practical applications,providing strong technical support for the safe operation of highspeed railway traction power supply systems.Originality/value–This study innovatively develops a 27.5kV high-voltage cable grounding current monitoring system,which provides a new technical means for evaluating the insulation status of cables by accurately measuring the grounding current.The design,experimental verification and application of this system in high-speed railway traction power supply systems have demonstrated significant academic value and practical significance,contributing innovative solutions to the field of railway power supply safety monitoring.
文摘The monitoring of soil moisture content in paddy field is one of important parts and contents of regional soil moisture monitoring. But a good monitoring scheme hasn’t been established. A real-time monitoring scheme of soil moisture content in paddy field was put forward from two key links of soil moisture content monitoring and field water-layer monitoring. This scheme could meet the alternative monitoring requirements of soil moisture content in water layer and none-water layer. It had a good maneuverability and could provide references for practical work.
文摘The assessment of the measurement error status of online Capacitor Voltage Transformers (CVT) within the power grid is of profound significance to the equitable trade of electric energy and the secure operation of the power grid. This paper advances an online CVT error state evaluation method, anchored in the in-phase relationship and outlier detection. Initially, this method leverages the in-phase relationship to obviate the influence of primary side fluctuations in the grid on assessment accuracy. Subsequently, Principal Component Analysis (PCA) is employed to meticulously disentangle the error change information inherent in the CVT from the measured values and to compute statistics that delineate the error state. Finally, the Local Outlier Factor (LOF) is deployed to discern outliers in the statistics, with thresholds serving to appraise the CVT error state. Experimental results incontrovertibly demonstrate the efficacy of this method, showcasing its prowess in effecting online tracking of CVT error changes and conducting error state assessments. The discernible enhancements in reliability, accuracy, and sensitivity are manifest, with the assessment accuracy reaching an exemplary 0.01%.
基金This work was partly supported by the National Natural Science Foundation of China(51675104 and 61703110)the Science and Technology Planning Project of Guangzhou,China(201707010197)+2 种基金the Innovation Team Project,Department of Education of Guangdong Province,China(2017KCXTD010)the Guangdong Provincial Natural Science Foundation of China(2017A030310494 and 2016A030310347)the Youth Science Foundation of Guangdong University of Technology(16ZK0010).
文摘In this research,an auxiliary illumination visual sensor system,an ultraviolet/visible(UVV)band visual sensor system(with a wavelength less than 780 nm),a spectrometer,and a photodiode are employed to capture insights into the high-power disc laser welding process.The features of the visible optical light signal and the reflected laser light signal are extracted by decomposing the original signal captured by the photodiode via the wavelet packet decomposition(WPD)method.The captured signals of the spectrometer mainly have a wavelength of 400-900 nm,and are divided into 25 sub-bands to extract the spectrum features by statistical methods.The features of the plume and spatters are acquired by images captured by the UVV visual sensor system,and the features of the keyhole are extracted from images captured by the auxiliary illumination visual sensor system.Based on these real-time quantized features of the welding process,a deep belief network(DBN)is established to monitor the welding status.A genetic algorithm is applied to optimize the parameters of the proposed DBN model.The established DBN model shows higher accuracy and robustness in monitoring welding status in comparison with a traditional back-propagation neural network(BPNN)model.The effectiveness and generalization ability of the proposed DBN are validated by three additional experiments with different welding parameters.
基金Projects(50874064,50804026,50904039)supported by the National Natural Science Foundation of ChinaProject(200804290002)supported by Research Fund for the Doctoral Program of Higher Education of ChinaProject(G2010F10)supported by S&T Plan Project from Shandong Provincial Education Department
文摘Considering its structural features, geometric shapes, service mode, environmental media, mechanical behavior, etc, the special nature and complexity of tailings dam were summarized. The technical approach to safety management for tailings dam was proposed, which is the on-line automated monitoring and early warning information. The results show that a strong theoretical basis can be provided for security monitoring and security management of tailings dam. Online automated monitoring system for tailings dam has full implementation of the information. It is applied widely in Lingnan gold mine, Xiadian gold mine and Hedong gold mine in Zhaoyuan, Shandong Province, and achieves good effect.
文摘Landslides have occurred frequently in the Luoshan mining area because of disordered mining.This paper discusses the landforms and physiognomy,hydro-meteorology,formation lithology,and geologic structure of the Luoshan mining area.It also describes the factors influencing the slope stability of landslide No.Ⅲ,determines the general parameters and typical section plane,analyzes the stress-strain state of the No.Ⅲ slope,and calculates its safety factors with FLAC3 D under saturated and natural conditions.Based on a stability analysis,a remote real-time monitoring system was applied to the No.Ⅲ slope,and these monitoring data were collected and analyzed.
基金financially supported by the National Key Research and Development Program of China(No.2019YFC1805400)。
文摘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%.
基金Supported by the National Natural Science Foundation of China （No.50378041） and the Specialized Research Fund for the Doctoral Program of Higher Education （No.2003487016）.
文摘Based on digital image processing technique, a real-time system is developed to monitor and detect the dynamic displacement of engineering structures. By processing pictures with a self-programmed software, the real-time coordinate of an object in a certain coordinate system can be obtained, and further dynamic displacement data and curve of the object can also be achieved. That is, automatic gathering and real-time processing of data can be carried out by this system simultaneously. For this system, first, an untouched monitoring technique is adopted, which can monitor or detect objects several to hundreds of meters apart; second, it has flexible installation condition and good monitoring precision of sub-millimeter degree; third, it is fit for dynamic, quasi-dynamic and static monitoring of large engineering structures. Through several tests and applications in large bridges, good reliability and dominance of the system is proved.
基金Funded by the National Science and Technology Support Project of China (No. 2006BAA03B02-03)
文摘A method based on solution of the inverse heat conduction problem was presented for online stress monitoring and fatigue life analysis of boiler drums. The mathematical model of the drum temperature distribution is based on the assumptions that the difference of temperature along the longitudinal axis of the boiler drum is negligible with changes only in the radial direction and the circumferential direction, and that the outer surface of drum is thermaUy insulated. Combining this model with the control-volume method provides temperatures at different points on a cross-section of the drum. With the temperature data, the stresses and the life expectancy of the boiler drum are derived according to the ASME code. Applying this method to the cold start-up process of a 300 MW boiler demonstrated the absence of errors caused by the boundary condition assumptions on the inner surface of the drum and testified that the method is an applicable technique for the online stress monitoring and fatigue life analysis of boiler drums.
基金Science and Technology Projects of Gansu Electric Power Company(No.52274514005W)
文摘The online-monitoring methods for insulation performance of current transformers of 330-750 kV substation are analyzed and compared.The effectiveness and availability of each method are discussed.Main features,advantages and disadvantages of each method and its corresponding standard are also described.
