Stability and accuracy of central difference method for real-time dynamic substructure testing considering mass participation coefficient

For real-time dynamic substructure testing(RTDST),the influence of the inertia force of fluid specimens on the stability and accuracy of the integration algorithms has never been investigated.Therefore,this study proposes to investigate the stability and accuracy of the central difference method(CDM)for RTDST considering the specimen mass participation coefficient.First,the theory of the CDM for RTDST is presented.Next,the stability and accuracy of the CDM for RTDST considering the specimen mass participation coefficient are investigated.Finally,numerical simulations and experimental tests are conducted for verifying the effectiveness of the method.The study indicates that the stability of the algorithm is affected by the mass participation coefficient of the specimen,and the stability limit first increases and then decreases as the mass participation coefficient increases.In most cases,the mass participation coefficient will increase the stability limit of the algorithm,but in specific circumstances,the algorithm may lose its stability.The stability and accuracy of the CDM considering the mass participation coefficient are verified by numerical simulations and experimental tests on a three-story frame structure with a tuned liquid damper.

Application of GNSS-PPP on Dynamic Deformation Monitoring of Offshore Platforms

The real-time dynamic deformation monitoring of offshore platforms under environmental excitation is crucial to their safe operation.Although Global Navigation Satellite System-Precise Point Positioning(GNSS-PPP)has been considered for this purpose,its monitoring accuracy is relatively low.Moreover,the influence of background noise on the dynamic monitoring accuracy of GNSS-PPP remains unclear.Hence,it is imperative to further validate the feasibility of GNSS-PPP for deformation monitoring of offshore platforms.To address these concerns,vibration table tests with different amplitudes and frequencies are conducted.The results demonstrate that GNSS-PPP can effectively monitor horizontal vibration displacement as low as±30 mm,which is consistent with GNSS-RTK.Furthermore,the spectral characteristic of background noise in GNSS-PPP is similar to that of GNSS-RTK(Real Time Kinematic).Building on this observation,an improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)has been proposed to de-noise the data and enhance the dynamic monitoring accuracy of GNSS-PPP.Field monitoring application research is also undertaken,successfully extracting and analyzing the dynamic deformation of an offshore platform structure under environmental excitation using GNSS-PPP monitoring in conjunction with improved CEEMDAN de-noising.By comparing the de-noised dynamic deformation trajectories of the offshore platform during different periods,it is observed that the platform exhibits reversible alternating vibration responses under environmental excitation,with more pronounced displacement deformation in the direction of load action.The research results confirm the feasibility and potential of GNSS-PPP for dynamic deformation monitoring of offshore platforms.

Nanomaterial-assisted wearable glucose biosensors for noninvasive real-time monitoring:Pioneering point-of-care and beyond

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.

Real-Time Monitoring Method for Cow Rumination Behavior Based on Edge Computing and Improved MobileNet v3

[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.

A process monitoring method for autoregressive-dynamic inner total latent structure projection

As a dynamic projection to latent structures(PLS)method with a good output prediction ability,dynamic inner PLS(DiPLS)is widely used in the prediction of key performance indi-cators.However,due to the oblique decomposition of the input space by DiPLS,there are false alarms in the actual industrial process during fault detection.To address the above problems,a dynamic modeling method based on autoregressive-dynamic inner total PLS(AR-DiTPLS)is proposed.The method first uses the regression relation matrix to decompose the input space orthogonally,which reduces useless information for the predic-tion output in the quality-related dynamic subspace.Then,a vector autoregressive model(VAR)is constructed for the predic-tion score to separate dynamic information and static informa-tion.Based on the VAR model,appropriate statistical indicators are further constructed for online monitoring,which reduces the occurrence of false alarms.The effectiveness of the method is verified by a Tennessee-Eastman industrial simulation process and a three-phase flow system.

Real-Time Monitoring of Meteorological Data at In-Situ GCW Remediation Sites

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.

A Real-Time System for Monitoring Distant Dynamic Displacement of Structures

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.

Microseismic monitoring and forecasting of dynamic disasters in underground hydropower projects in southwest China:A review

The underground hydropower projects in Southwest China is characterized by large excavation sizes,high geostresses,complicated geological conditions and multiple construction processes.Various disasters such as collapses,large deformations,rockbursts are frequently encountered,resulting in serious casualties and huge economic losses.This review mainly presents some representative results on microseismic(MS)monitoring and forecasting for disasters in hydropower underground engineering.First,a set of new denoising,spectral analysis,and location methods were developed for better identification and location of MS signals.Then,the tempo-spatial characteristics of MS events were analyzed to understand the relationship between field construction and damages of surrounding rocks.Combined with field construction,geological data,numerical simulation and parametric analysis of MS sources,the focal mechanism of MS events was revealed.A damage constitutive model considering MS fracturing size was put forward and feedback analysis considering the MS damage of underground surrounding rocks was conducted.Next,an MS multi-parameter based risk assessment and early warning method for dynamic disasters were proposed.The technology for control of the damage and deformation of underground surrounding rocks was proposed for underground caverns.Finally,two typical underground powerhouses were selected as case studies.These achievements can provide significant references for prevention and control of dynamic disasters for underground engineering with similar complicated geological conditions.

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.

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.

Mechanism,prevention,and control of mining-induced dynamic disasters in underground metal mines in China:Challenges and solutions

Metal mineral resources play an indispensable role in the development of the national economy.Dynamic disasters in underground metal mines seriously threaten mining safety,which are major scientific and technological problems to be solved urgently.In this article,the occurrence status and grand challenges of some typical dynamic disasters involving roof falling,spalling,collapse,large deformation,rockburst,surface subsidence,and water inrush in metal mines in China are systematically presented,the characteristics of mining-induced dynamic disasters are analyzed,the examples of dynamic disasters occurring in some metal mines in China are summarized,the occurrence mechanism,monitoring and early warning methods,and prevention and control techniques of these disasters are highlighted,and some new opinions,suggestions,and solutions are proposed simultaneously.Moreover,some shortcomings in current disaster research are pointed out,and the direction of efforts to improve the prevention and control level of dynamic disasters in China’s metal mines in the future is prospected.The integration of forward-looking key innovative theories and technologies in the abovementioned aspects will greatly enhance the cognitive level of disaster prevention and mitigation in China’s metal mining industry and achieve a significant shift from passive disaster relief to active disaster prevention.

Time-Lapse Full-Waveform Inversion Using Cross-Correlation-Based Dynamic Time Warping

Offshore carbon capture, utilization, and storage(OCCUS) is regarded as a crucial technology for mitigating greenhouse gas emissions.Quantitative monitoring maps of sealed carbon dioxide are necessary in a comprehensive OCCUS project. A potential high-resolution method for the aforementioned purpose lies in the full-waveform inversion(FWI) of time-lapse seismic data. However, practical applications of FWI are severely restricted by the well-known cycle-skipping problem. A new time-lapse FWI method using cross-correlation-based dynamic time warping(CDTW) is proposed to detect changes in the subsurface property due to carbon dioxide(CO_(2)) injection and address the aforementioned issue. The proposed method, namely CDTW, which combines the advantages of cross-correlation and dynamic time warping, is employed in the automatic estimation of the discrepancy between the seismic signals simulated using the baseline/initial model and those acquired. The proposed FWI method can then back-project the estimated discrepancy to the subsurface space domain, thereby facilitating retrieval of the induced subsurface property change by taking the difference between the inverted baseline and monitor models. Numerical results on pairs of signals prove that CDTW can obtain reliable shifts under amplitude modulation and noise contamination conditions. The performance of CDTW substantially outperforms that of the conventional dynamic time warping method. The proposed time-lapse fullwaveform inversion(FWI) method is applied to the Frio-2 CO_(2) storage model. The baseline and monitor models are inverted from the corresponding time-lapse seismic data. The changes in velocity due to CO_(2) injection are reconstructed by the difference between the baseline and the monitor models.

Efficient Route Planning for Real-Time Demand-Responsive Transit

Demand Responsive Transit (DRT) responds to the dynamic users’ requests without any fixed routes and timetablesand determines the stop and the start according to the demands. This study explores the optimization of dynamicvehicle scheduling and real-time route planning in urban public transportation systems, with a focus on busservices. It addresses the limitations of current shared mobility routing algorithms, which are primarily designedfor simpler, single origin/destination scenarios, and do not meet the complex demands of bus transit systems. Theresearch introduces an route planning algorithm designed to dynamically accommodate passenger travel needsand enable real-time route modifications. Unlike traditional methods, this algorithm leverages a queue-based,multi-objective heuristic A∗ approach, offering a solution to the inflexibility and limited coverage of suburbanbus routes. Also, this study conducts a comparative analysis of the proposed algorithm with solutions based onGenetic Algorithm (GA) and Ant Colony Optimization Algorithm (ACO), focusing on calculation time, routelength, passenger waiting time, boarding time, and detour rate. The findings demonstrate that the proposedalgorithmsignificantly enhances route planning speed, achieving an 80–100-fold increase in efficiency over existingmodels, thereby supporting the real-time demands of Demand-Responsive Transportation (DRT) systems. Thestudy concludes that this algorithm not only optimizes route planning in bus transit but also presents a scalablesolution for improving urban mobility.

Emotionally Resonant Branding: The Role of AI in Synthesising Dynamic Brand Images for Artists in the Music Industry

Artificial Intelligence (AI) expands its recognition rapidly through the past few years in the context of generating content dynamically, remarkably challenging the human creativity. This study aims to evaluate the efficacy of AI in enhancing personal branding for musicians, particularly in crafting brand images based on emotions received from the artist’s music will improve the audience perceptions regarding the artist’s brand. Study used a quantitative approach for the research, gathering primary data from the survey of 191 people—music lovers, musicians and music producers. The survey focuses on preferences, perceptions, and behaviours related to music consumption and artist branding. The study results demonstrate the awareness and understanding of AI’s role in personal branding within the music industry. Also, results indicate that such an adaptive approach enhances audience perceptions of the artist and strengthens emotional connections. Furthermore, over 50% of the participants indicated a desire to attend live events where an artist’s brand image adapts dynamically to their emotions. The study focuses on novel approaches in personal branding based on the interaction of AI-driven emotional data. In contrast to traditional branding concepts, this study indicates that AI can suggest dynamic and emotionally resonant brand identities for artists. The real time audience response gives proper guidance for the decision-making. This study enriches the knowledge of AI’s applicability to branding processes in the context of the music industry and opens the possibilities for additional advancements in building emotionally appealing brand identities.

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.

Delay-dependent stability and added damping of SDOF real-time dynamic hybrid testing

It is well-recognized that a transfer system response delay that reduces the test stability inevitably exists in real-time dynamic hybrid testing （RTDHT）. This paper focuses on the delay-dependent stability and added damping of SDOF systems in RTDHT. The exponential delay term is transferred into a rational fraction by the Pad6 approximation, and the delay-dependent stability conditions and instability mechanism of SDOF RTDHT systems are investigated by the root locus technique. First, the stability conditions are discussed separately for the cases of stiffness, mass, and damping experimental substructure. The use of root locus plots shows that the added damping effect and instability mechanism for mass are different from those for stiffness. For the stiffness experimental substructure case, the instability results from the inherent mode because of an obvious negative damping effect of the delay. For the mass case, the delay introduces an equivalent positive damping into the inherent mode, and instability occurs at an added high frequency mode. Then, the compound stability condition is investigated for a general case and the results show that the mass ratio may have both upper and lower limits to remain stable. Finally, a high-emulational virtual shaking table model is built to validate the stability conclusions.

Simulation of large-scale numerical substructure in real-time dynamic hybrid testing

A solution scheme is proposed in this paper for an existing RTDHT system to simulate large-scale finite element （FE） numerical substructures. The analysis of the FE numerical substructure is split into response analysis and signal generation tasks, and executed in two different target computers in real-time. One target computer implements the response analysis task, wherein a large time-step is used to solve the FE substructure, and another target computer implements the signal generation task, wherein an interpolation program is used to generate control signals in a small time-step to meet the input demand of the controller. By using this strategy, the scale of the FE numerical substructure simulation may be increased significantly. The proposed scheme is initially verified by two FE numerical substructure models with 98 and 1240 degrees of freedom （DOFs）. Thereafter, RTDHTs of a single frame-foundation structure are implemented where the foundation, considered as the numerical substructure, is simulated by the FE model with 1240 DOFs. Good agreements between the results of the RTDHT and those from the FE analysis in ABAQUS are obtained.

A Concept of Dynamically Reconfigurable Real-time Vision System for Autonomous Mobile Robotics

This paper describes specific constraints of vision systems that are dedicated to be embedded in mobile robots. If PC-based hardware architecture is convenient in this field because of its versatility, flexibility, performance, and cost, current real-time operating systems are not completely adapted to long processing with varying duration, and it is often necessary to oversize the system to guarantee fail-safe functioning. Also, interactions with other robotic tasks having more priority are difficult to handle. To answer this problem, we have developed a dynamically reconfigurable vision processing system, based on the innovative features of Cleopatre real-time applicative layer concerning scheduling and fault tolerance. This framework allows to define emergency and optional tasks to ensure a minimal quality of service for the other subsystems of the robot, while allowing to adapt dynamically vision processing chain to an exceptional overlasting vision process or processor overload. Thus, it allows a better cohabitation of several subsystems in a single hardware, and to develop less expensive but safe systems, as they will be designed for the regular case and not rare exceptional ones. Finally, it brings a new way to think and develop vision systems, with pairs of complementary operators.

SWARM INTELLIGENCE BASED DYNAMIC REAL-TIME SCHEDULING APPROACH FOR SEMICONDUCTOR WAFER FAB

Based on the analysis of collective activities of ant colonies, the typicalexample of swarm intelligence, a new approach to construct swarm intelligence basedmulti-agent-system (SMAS) for dynamic real-time scheduling for semiconductor wafer fab is proposed.The relevant algorithm, pheromone-based dynamic real-time scheduling algorithm (PBDR), is given.MIMAC test bed data set mini-fab is used to compare PBDR with FIFO (first in first out),SRPT(shortest remaining processing time) and CR(critical ratio) under three different release rules,i.e. deterministic rule, Poisson rule and CONWIP (constant WIP). It is shown that PBDR is prior toFIFO, SRPT and CR with better performance of cycle time, throughput, and on-time delivery,especially for on-time delivery performance.

A Class of Parallel Algorithms of Real-TimeNumerical Simulation for Stiff Dynamic System

In this paper a class of real-time parallel modified Rosenbrock methods of numerical simulation is constructed for stiff dynamic systems on a multiprocessor system, and convergence and numerical stability of these methods are discussed. A-stable real-time parallel formula of two-stage third-order and A(α)-stable real-time parallel formula with o ≈ 89.96° of three-stage fourth-order are particularly given. The numerical simulation experiments in parallel environment show that the class of algorithms is efficient and applicable, with greater speedup.