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.

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.

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.

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.

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.

On-line real-time path planning of mobile robots in dynamic uncertain environment

A new path planning method for mobile robots in globally unknown environment with moving obstacles is pre- sented. With an autoregressive (AR) model to predict the future positions of moving obstacles, and the predicted position taken as the next position of moving obstacles, a motion path in dynamic uncertain environment is planned by means of an on-line real-time path planning technique based on polar coordinates in which the desirable direction angle is taken into consideration as an optimization index. The effectiveness, feasibility, high stability, perfect performance of obstacle avoidance, real-time and optimization capability are demonstrated by simulation examples.

Design and validation of real-time dynamic spectrum management in OFDM-based HNPLC systems

Time-varying frequency selective attenuation and colored noises are unfavorable characteristics of power line communication(PLC) channels of the low voltage networks.To overcome these disadvantages,a novel real-time dynamic spectrum management(DSM) algorithm in orthogonal frequency division multiplexing(OFDM)-based high-speed narrow-band power line communication(HNPLC) systems is proposed,and the corresponding FPGA circuit is designed and realized.Performance of the proposed DSM is validated with a large amount of network experiments under practical PLC circumstance.As the noise in each narrow subcarrier is approximately Gaussian,the proposed DSM adopts the BER/SER expression formulized via the AWGN channel to provide a handy and universal strategy for power allocation.The real-time requirement is guaranteed by choosing subcarriers in group and employing the same modulation scheme within each transmission.These measures are suitable for any modulation scheme no matter the system criterion is to maximize data rate or minimize power/BER.Algorithm design and hardware implementation of the proposed DSM are given with some flexible and efficient conversions.The DSM circuit is carried out with Xilinx KC705.Simulation and practical experiments validate that the proposed real-time DSM significantly improves system performance.

Dynamic Power Dissipation Control Method for Real-Time Processors Based on Hardware Multithreading

In order to eliminate the energy waste caused by the traditional static hardware multithreaded processor used in real-time embedded system working in the low workload situation, the energy efficiency of the hardware multithread is discussed and a novel dynamic multithreaded architecture is proposed. The proposed architecture saves the energy wasted by removing idle threads without manipulation on the original architecture, fulfills a seamless switching mechanism which protects active threads and avoids pipeline stall during power mode switching. The report of an implemented dynamic multithreaded processor with 45 nm process from synthesis tool indicates that the area of dynamic multithreaded architecture is only 2.27% higher than the static one in achieving dynamic power dissipation, and consumes 1.3% more power in the same peak performance.

A dynamic fusion path planning algorithm for mobile robots incorporating improved IB-RRT∗and deep reinforcement learning

Dynamic path planning is crucial for mobile robots to navigate successfully in unstructured envi-ronments.To achieve globally optimal path and real-time dynamic obstacle avoidance during the movement,a dynamic path planning algorithm incorporating improved IB-RRT∗and deep reinforce-ment learning(DRL)is proposed.Firstly,an improved IB-RRT∗algorithm is proposed for global path planning by combining double elliptic subset sampling and probabilistic central circle target bi-as.Then,to tackle the slow response to dynamic obstacles and inadequate obstacle avoidance of tra-ditional local path planning algorithms,deep reinforcement learning is utilized to predict the move-ment trend of dynamic obstacles,leading to a dynamic fusion path planning.Finally,the simulation and experiment results demonstrate that the proposed improved IB-RRT∗algorithm has higher con-vergence speed and search efficiency compared with traditional Bi-RRT∗,Informed-RRT∗,and IB-RRT∗algorithms.Furthermore,the proposed fusion algorithm can effectively perform real-time obsta-cle avoidance and navigation tasks for mobile robots in unstructured environments.

Real-time Risk-averse Dispatch of an Integrated Electricity and Natural Gas System via Condi-tional Value-at-risk-based Lookup-table Ap-proximate Dynamic Programming

The real-time risk-averse dispatch problem of an integrated electricity and natural gas system(IEGS)is studied in this paper.It is formulated as a real-time conditional value-at-risk(CVaR)-based risk-averse dis-patch model in the Markov decision process framework.Because of its stochasticity,nonconvexity and nonlinearity,the model is difficult to analyze by traditional algorithms in an acceptable time.To address this non-deterministic polynomial-hard problem,a CVaR-based lookup-table approximate dynamic programming(CVaR-ADP)algo-rithm is proposed,and the risk-averse dispatch problem is decoupled into a series of tractable subproblems.The line pack is used as the state variable to describe the impact of one period’s decision on the future.This facilitates the reduction of load shedding and wind power curtailment.Through the proposed method,real-time decisions can be made according to the current information,while the value functions can be used to overview the whole opti-mization horizon to balance the current cost and future risk loss.Numerical simulations indicate that the pro-posed method can effectively measure and control the risk costs in extreme scenarios.Moreover,the decisions can be made within 10 s,which meets the requirement of the real-time dispatch of an IEGS.Index Terms—Integrated electricity and natural gas system,approximate dynamic programming,real-time dispatch,risk-averse,conditional value-at-risk.

Construction Simulation and Real-Time Control for High Arch Dam

A method of combining dynamic simulation with real-time control was proposed to fit the randomness and uncertainty in the high arch dam construction process. The mathematical logic model of high arch dam construction process was established. By combining dynamic construction simulation with schedule analysis, the process of construction schedule forecasting and analysis based on dynamic simulation was studied. The process of real-time schedule control was constructed and some measures for dynamic adjustment and control of construction schedule were provided. A system developed with the method is utilized in a being constructed hydroelectric project located at the Yellow River in northwest China, which can make the pouring plan of the dam in the next stage (a month, quarter or year) to guide the practical construction. The application result shows that the system provides an effective technical support for the construction and management of the dam.

Model-based framework for multi-axial real-time hybrid simulation testing

Real-time hybrid simulation is an efficient and cost-effective dynamic testing technique for performance evaluation of structural systems subjected to earthquake loading with rate-dependent behavior. A loading assembly with multiple actuators is required to impose realistic boundary conditions on physical specimens. However, such a testing system is expected to exhibit significant dynamic coupling of the actuators and suffer from time lags that are associated with the dynamics of the servo-hydraulic system, as well as control-structure interaction （CSI）. One approach to reducing experimental errors considers a multi-input, multi-output （MIMO） controller design, yielding accurate reference tracking and noise rejection. In this paper, a framework for multi-axial real-time hybrid simulation （maRTHS） testing is presented. The methodology employs a real-time feedback-feedforward controller for multiple actuators commanded in Cartesian coordinates. Kinematic transformations between actuator space and Cartesian space are derived for all six-degrees-of- freedom of the moving platform. Then, a frequency domain identification technique is used to develop an accurate MIMO transfer function of the system. Further, a Cartesian-domain model-based feedforward-feedback controller is implemented for time lag compensation and to increase the robustness of the reference tracking for given model uncertainty. The framework is implemented using the 1/5th-scale Load and Boundary Condition Box （LBCB） located at the University of Illinois at Urbana- Champaign. To demonstrate the efficacy of the proposed methodology, a single-story frame subjected to earthquake loading is tested. One of the columns in the fraane is represented physically in the laboratory as a cantilevered steel column. For real- time execution, the numerical substructure, kinematic transformations, and controllers are implemented on a digital signal processor. Results show excellent performance of the maRTHS framework when six-degrees-of-freedom are controUed at the interface between substructures.

A Class of Fast Algorithms in Real-Time Simulation

A class of hybrid algorithms of real-time simulation based on evaluation of non-integerstep right-hand side function are presented in this paper. And some results of the convergence and stability of the algorithms are given. Using the class of algorithms, evaluation for the right-hand side function is needed once in every integration-step. Moreover, comparing with the other methods with the same amount of work, their numerical stability regions are larger and the method errors are smaller, and the numerical experiments show that the algorithms are very effective.

Real-time hybrid simulation for structural control performance assessment

Real-time hybrid simulation is an attractive method to evaluate the response of structures under earthquake loads. The method is a variation of the pseudodynamic testing technique in which the experiment is executed in real time, thus allowing investigation of structural systems with rate-dependent components. Real-time hybrid simulation is challenging because it requires performance of all calculations, application of displacements, and acquisition of measured forces, within a very small increment of time. Furthermore, unless appropriate compensation for actuator dynamics is implemented, stability problems are likely to occur during the experiment. This paper presents an approach for real-time hybrid simulation in which compensation for actuator dynamics is implemented using a model-based feedforward compensator. The method is used to evaluate the response of a semi-active control of a structure employing an MR damper. Experimental results show good agreement with the predicted responses, demonstrating the effectiveness of the method for structural control performance assessment.

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.

A NEW HYBRID WAVELET-KALMAN FILTER METHOD FOR THE ESTIMATION OF DYNAMIC SYSTEM

A new hybrid wavelet-Kalman filter method for the estimation of dynamic system is developed, Using this method, the real-time multiscale estimation of the dynamic system is implemented, and the observation equation established is for the object signal itself rather than its wavelet decomposition. The simulation results show that this method can be used to estimate the object's state of the stacked system, which is the foundation of multiscale data fusion; besides the performance of the new algorithm developed in the letter is almost optimal.