Dynamic Response Capability Research Articles

A Hybrid Framework for Securing 5G-Enabled Healthcare Systems

The rapid adoption of 5G technology in healthcare introduces significant challenges regarding data privacy and security. This paper proposes a hybrid framework integrating blockchain, zero-trust architecture (ZTA), and AI-driven threat detection to address these challenges. Blockchain ensures secure, tamper-proof data storage, while ZTA strengthens access control by continuously verifying users and devices. AI contributes by providing real-time threat detection and dynamic response capabilities, making the system more resilient to evolving cyber risks. A systematic literature review was conducted to analyze existing frameworks and identify gaps in 5G healthcare security. The findings reveal that while individual technologies such as blockchain and ZTA are well-established, their integration into a cohesive framework remains underexplored. The proposed hybrid solution effectively mitigates the risks associated with 5G networks by offering a multi-layered security approach. This research contributes to the field by proposing a scalable, adaptable security model suitable for 5G-enabled healthcare systems. Future research should focus on empirical validation, scalability testing, and exploring lightweight alternatives to blockchain and AI for resource-constrained environments. Additionally, investigating the integration of emerging technologies like quantum computing and 6G networks will further enhance the framework’s security capabilities. This study provides a foundation for developing secure, privacy-preserving systems for healthcare in the 5G era.

Application of artificial intelligence based on state grid ESG platform in clean energy scheduling optimization

The randomness and volatility of existing clean energy sources have increased the complexity of grid scheduling. To address this issue, this work proposes an artificial intelligence (AI) empowered method based on the Environmental, Social, and Governance (ESG) big data platform, focusing on multi-objective scheduling optimization for clean energy. This work employs a combination of Particle Swarm Optimization (PSO) and Deep Q-Network (DQN) to enhance grid scheduling efficiency and clean energy utilization. First, the work analyzes the complexity and uncertainty challenges faced in clean energy scheduling within the current power system, highlighting the limitations of traditional methods in handling multi-objective optimization and real-time response. Consequently, the work introduces the ESG big data platform and leverages its abundant data resources and computational power to improve the scheduling decision-making process. Next, PSO is used for initial scheduling optimization and a mathematical model for clean energy scheduling optimization is constructed. To further enhance the dynamic response capability of the scheduling process, this work designs a dual-layer architecture. Among this architecture, DQN is responsible for adjusting the PSO initially optimized scheduling plan based on real-time data during the actual scheduling process, adapting it to the instantaneous change demand and renewable energy output characteristics. Finally, to verify the model’s effectiveness, this work conducts simulation analyses using real data from the state grid ESG big data platform. The results indicate that this method significantly improves clean energy utilization, increasing it from 62.4% (with traditional methods) to 87.7%, while reducing scheduling costs by 22%. With the increase in communication delay, the generation time of scheduling instruction increases significantly. This is because the algorithm needs to wait longer to receive the complete grid status information, which affects the generation speed of scheduling instructions. Moreover, this method demonstrates good adaptability and stability under different load demands and clean energy supply conditions. This work introduces a novel and efficient method for clean energy scheduling optimization by integrating PSO and DQN. It contributes to the overall improvement of clean energy utilization within the State Grid and provides a theoretical and empirical foundation for further research in related fields.

How to Evaluate the Operating Performance of Mid-Deep Geothermal Heat Pump Systems (MD-GHPs): A Study on a Multistage Evaluation Index System

Mid-deep geothermal heat pump systems (MD-GHPs) use mid-deep borehole heat exchangers (MDBHEs) to extract heat from the geothermal energy at a depth of 2–3 km, and have been used for space heating in China over the last decade. This paper proposes a comprehensive and multilevel evaluation-index system to analyze and evaluate the energy performance of MD-GHPs. The multilevel evaluation index system consists of a target layer, a criterion layer, and an index layer, where the criterion layer is subdivided into six aspects and the index layer includes 26 specific indices, reflecting the geothermal resources, heat transfer performance of the MDBHEs, energy efficiency of the heat pump systems, building space heating demand, grid dynamic response capability, and energy-saving and economic benefits. Then, based on both expert survey results and case study data, the entropy weight method and the analytic hierarchy process are integrated to determine indicator weight coefficients among the multilevel evaluation indices, comprehensively considering both subjective and objective analyses. Furthermore, a fuzzy comprehensive evaluation model is conducted to integrate these weighted indices into a multi-criteria evaluation of MD-GHP performance. Finally, the proposed method was applied to evaluate the practical performance of four projects, returning scores of 61.56, 58.33, 72.73, and 78.41. These evaluations enable an overall assessment of the energy performance of MD-GHPs, reflecting the technical weaknesses and offering optimization guidance for system design and operation.

Applied Research on the Impact of Big Data, Cloud Computing and Cloud Storage on Smart Manufacturing Supply Chain

The rapid advancements in big data, cloud computing, and cloud storage technologies are profoundly reshaping the operational models of smart manufacturing supply chains. The integration and application of these technologies not only drive the enhanced data collection and analysis capabilities across various supply chain segments but also significantly improve the accuracy of demand forecasting, the rationality of resource allocation, and the scientific nature of decision support. Consequently, the collaborative efficiency and dynamic response capabilities of supply chains have been substantially elevated, providing robust technological backing for enterprises in navigating the intricately volatile market environment. Nonetheless, while reaping the benefits of these technological advancements, challenges such as data security and privacy protection, system integration, and technical selection cannot be overlooked. By judiciously selecting technology platforms, reinforcing data security management, and continuously investing in technological advancements and talent cultivation, the holistic effectiveness of smart manufacturing supply chains will be further optimized.

Enhanced Short-Term Temperature Prediction of Seasonally Frozen Soil Subgrades Using the NARX Neural Network

Accurate prediction of subgrade temperatures in seasonally frozen regions is crucial for understanding thermal states, frost heave phenomena, stability, and other critical characteristics. This study employs a nonlinear autoregressive with exogenous input (NARX) network to predict short-term subgrade temperatures in the Golmud-Nagqu section of China’s National Highway 109. The methodology involves preprocessing subgrade monitoring data, including temperature, water content, and frost heave, followed by developing a temperature prediction model. This tailored NARX neural network, compared to the traditional BP neural network, integrates feedback and delay mechanisms for monitoring data, offering superior memory and dynamic response capabilities. The precision of the NARX model is assessed with the backpropagation (BP) network, indicating that the NARX neural network significantly outperforms the BP model in both precision and stability for temperature prediction in seasonally frozen subgrades. These findings suggest that the NARX model is a valuable tool for accurately predicting subgrade temperatures in seasonally frozen regions, offering significant insights for practical engineering applications.

Research on position tracking control of electro-hydraulic actuator system combining feedforward inverse model controller and exponential functional link network adaptive controller

The electro-hydraulic actuator system plays a crucial role in indoor high-frequency simulation experiments. Many components of the system exhibit unavoidable nonlinear characteristics, which reduce its dynamic position response capability. Additionally, most algorithms are unsuitable for practical high-frequency conditions due to either local instability or high computational complexity. To improve position tracking accuracy under high-frequency control, a novel combined control strategy is proposed, integrating an appropriate feedforward inverse model controller (FFIMC) with an exponential functional link network adaptive controller (EFLNAC). Initially, the system has reached a stable state under the action of the PID controller. Subsequently, the FFIMC estimates both the model and the inverse model of the PID system by applying the AIC technique developed through the DCT-VSSNLMS adaptive algorithm, which extends the system’s bandwidth. Following this, the EFLNAC, as the outermost loop, acquires the inverse model of the FFIMC system and updates internal parameters in real-time, thereby enhancing the system’s resistance to nonlinear disturbances. Finally, the combined controller is validated through simulations and experiments under various working conditions. The results show a significant increase in the PID system’s bandwidth and demonstrate that the tracking error of the signal is maintained within 0.079 mm. Therefore, the combined controller proves to be an effective solution for high-precision, high-frequency displacement tracking, offering the advantages of low computational complexity, high accuracy, and strong effectiveness.

Current Stress Minimization Based on Particle Swarm Optimization for Dual Active Bridge DC–DC Converter

Under extended-phase-shift (ESP) control, the current stress of the dual active bridge converter (DAB) is relatively high, which reduces the efficiency of the converter. To solve this problem, a particle swarm optimization (PSO) algorithm based on minimizing the current stress is proposed in this paper. The optimal phase-shift ratio of the DAB converter with ESP control is obtained by using the algorithm’s optimization characteristic. This approach ensures that the converter achieves minimal current stress, thereby enhancing the steady-state performance of the DAB converter. Moreover, in terms of dynamic performance, traditional PI control has poor dynamic response ability when there are sudden changes in load and input voltage. To solve this problem, the voltage dynamic matrix control (DMC) algorithm is introduced to combine with the PSO algorithm to minimize the current stress of the DAB converter under EPS control while enhancing the dynamic response capability of the DAB converter. A simulation model was constructed for comparative validation on MATLAB/Simulink 2019, demonstrating the correctness and effectiveness of the improved control method.

Feedforward Control Strategy of a DC-DC Converter for an Off-Grid Hydrogen Production System Based on a Linear Extended State Observer and Super-Twisting Sliding Mode Control

With the large-scale integration of renewable energy into off-grid DC systems, the stability issues caused by their fluctuations have become increasingly prominent. The dual active bridge (DAB) converter, as a DC-DC converter suitable for high power and high voltage level off-grid DC systems, plays a crucial role in maintaining and regulating grid stability through its control methods. However, the existing control methods for DAB are inadequate: linear control fails to meet dynamic response requirements, while nonlinear control relies on detailed model structures and parameters, making the control design complex and less accurate. To address this issue, this paper proposes a feedforward control strategy for a DC-DC converter in an off-grid hydrogen production system based on a linear extended state observer (LESO) and super-twisting sliding mode control (STSMC). Firstly, a reduced-order simplified model of the DAB was constructed through the structure of DAB. Then, based on the reduced-order simplified model, a feedforward control based on LESO and STSMC was designed, and its stability was analyzed. Finally, a simulation comparison of PI, LESO + terminal sliding mode control (TSMC), and LESO + STSMC control methods was conducted in a DC off-grid hydrogen production system. The results verified the proposed control method’s enhancement of the DAB’s rapid dynamic response capability and the system’s transient stability.

Assembly and operation optimization of proton exchange membrane water electrolyzer for performance enhancement

Abstract Proton exchange membrane water electrolysis (PEMWE) for hydrogen production possesses wide-ranging and rapid dynamic response capabilities, offering promising applications in the consumption of new energy sources and the dynamic balancing of power grids with a high proportion of renewable energy. The assembly and operating conditions of PEMWE significantly affect its performance. Therefore, thoroughly studying and understanding the influence of PEMWE’s assembly and operating conditions on water electrolysis performance are crucial for enhancing PEMWE’s performance and promoting its applications. In this research, the influence of installation preload torque, feedwater flow rate, and operating temperature on the performance of the electrolyzer, including the electrochemical active specific surface area (ECSA), high-frequency resistance (HFR) and various types of polarizations (including activation polarization, ohmic polarization, and mass transfer polarization) during the operation of the electrolyzer, were detailedly investigated. The results showed that the optimal preload torque and operating temperature for the electrolyzer were 3 Nm and 80°C. Under these conditions, an optimized PEMWE performance would be achieved by ensuring that the feedwater flow rate exceeded the water consumption during electrolysis.

Action of liquid tune mass dampers and base isolation in high rise buildings

In areas where earthquakes are common, high-rise structure seismic resistance is a major concern. The purpose of this research is to determine whether base isolation and liquid tuned mass dampers (LTMDs) can improve tall building seismic performance. A symmetrical G+8 story RCC structure in Zone V with medium grade soil is analyzed using ETABS software, Response Spectrum Analysis, and Equivalent Static Analysis to see how the structure behaves under different load combinations. For various structural configure rations, including basic models, models with water tanks, and models with base isolation, key factors such joint displacement, storey drift, base shear, and time period are evaluated. The results show that as compared to baseline models, models with damping and isolation systems exhibit much lower joint displacements, base shear forces, and story drift. In particular, base isolation models perform better in terms of reducing structural deformations and resisting lateral forces. Furthermore, shorter time periods and higher frequencies are routinely observed in base isolated models, suggesting enhanced dynamic response and energy dissipation capabilities. The paper also covers the flexibility and benefits of using LTMDs and base isolation to reduce seismic threats. Buildings can be successfully separated from ground motion by base isolation systems, and dynamic response can be countered by LTMDs using regulated liquid sloshing. The attraction of LTMDs in improving overall stability is increased by their adaptability in minimizing torsional and lateral vibrations. The study concludes by emphasizing how crucial it is to use cutting-edge structural retrofitting methods, including base isolation and LTMDs, to improve the seismic resistance of high-rise structures. In earthquake-prone areas, engineers and legislators may proactively protect tall buildings and guarantee a safer, more robust built environment by incorporating these technologies into construction standards and design procedures.

Energy and fluid dynamics of PEM fuel cells in automotive applications: integration strategies for enhanced performance

Fuel cells using proton exchange membranes, or PEMs, are leading the way in the adoption of sustainable vehicle technology. This study investigates the synergistic integration of PEM fuel cells with advanced battery systems in fuel cell vehicles (FCVs) to enhance energy management and efficiency. By implementing a hybrid energy management strategy that combines the consistent power output of PEM fuel cells with the dynamic response capabilities of lithium-ion batteries, the system achieves a 15% increase in fuel efficiency and a 20% reduction in hydrogen consumption compared to conventional configurations. Simulations conducted over various driving cycles, including urban, suburban, and highway scenarios, reveal that this hybrid setup not only improves operational flexibility but also reduces the dependency on the PEM fuel cell during low-demand periods by up to 30%. These results imply that the suggested integration approach greatly improves the sustainability and overall performance of FCVs, establishing them as a workable option for lowering emissions associated with transportation.

Position Servo Control of Electromotive Valve Driven by Centralized Winding LATM Using a Kalman Filter Based Load Observer

The exhaust gas recirculation (EGR) valve plays an important role in improving engine fuel economy and reducing emissions. In order to improve the positioning accuracy and robustness of the EGR valve under uncertain dynamics and external disturbances, this paper proposes a positioning servo system design for an electromotive (EM) EGR valve based on the Kalman filter. Taking a novel valve driven by a central winding limited angle torque motor (LATM) as the object, we have fully considered the influence of the motor rotor position and load current, as well as the magnetic field saturation and cogging effect, improved the existing LTAM model, and derived accurate torque expression. The parameter uncertainty of the above internal model and the external stochastic disturbance were unified as “total disturbance”, and a Kalman filter-based observer was designed for disturbance estimations and real-time feed-forward compensation. Furthermore, using non-contact magnetic angle measurements to obtain accurate valve position information, a position control model with real-time response and high accuracy was established. Numerous simulated and experimental data show that in the presence of ± 25% plant model parameter fluctuations and random shock-type disturbances, the servo system scheme proposed in this paper achieves a maximum position deviation of 0.3 mm, a repeatability of positioning accuracy after disturbances of 0.01 mm, and a disturbance recovery time of not more than 250 ms. In addition, the above performance is insensitive to the duration of the disturbance, which demonstrates the strong robustness, high accuracy, and excellent dynamic response capability of the proposed design.

An innovative 3D wave channel design for boosting the dynamic response of proton exchange membrane fuel cell stack

The dynamic response of proton exchange membrane fuel cell (PEMFC) is a crucial metric for evaluating these systems in vehicular applications. The forced convection effect can significantly enhance the dynamic response capabilities of proton exchange membrane fuel cells. In this study, we propose the application of the forced convection effect within a parallel flow field. This approach retains the advantages of low pressure drop at the inlets and outlets of the parallel flow field and facilitates rapid distribution of reaction gases, while simultaneously enhancing the dynamic response of actual PEMFC stacks without compromising drainage efficiency. we propose an innovative 3D wave channel design for PEMFC. Specifically, we design and assemble a stack optimized for performance. The proposed design is tested against the traditional 2D straight channel stack, with the 3D wave channel stack demonstrating an increase in peak power density by 8.05% to 33.92% across a relative humidity range of 20% to 100%. This novel design outperforms the 2D straight channel design in terms of gas distribution uniformity, and enhancedmass transfer to the diffusion layer, as well as reduction of concentration polarization in the PEMFC. It also enhances self-humidification under low humidity conditions. Most importantly, the 3D wave channel stack exhibits a superior dynamic response, with voltage fluctuations after current loading being 74.36% to 91.15% lower than those of the 2D straight channel stack. Furthermore, the 3D wave channel stack maintains superior voltage stability under starvation conditions. The findings from this study contribute significantly to optimizing the dynamic response capability of PEMFC systems, highlighting the potential of the 3D wave channel design in enhancing PEMFC performance.

Dynamic monitoring of a 3D-printed airway tissue model using an organic electrochemical transistor

Assessing the transepithelial resistance to ion flow in the presence of an electric field enables the evaluation of the integrity of the epithelial cell layer. In this study, we introduce an organic electrochemical transistor (OECT) interfaced with a 3D living tissue, designed to monitor the electrical resistance of cellular barriers in real-time. We have developed a non-invasive, tissue-sensing platform by integrating an inkjet-printed large-area OECT with a 3D-bioprinted multilayered airway tissue. This unique configuration enables the evaluation of epithelial barrier integrity through the dynamic response capabilities of the OECT. Our system effectively tracks the formation and integrity of 3D-printed airway tissues in both liquid-liquid and air-liquid interface culture environments. Furthermore, we successfully quantified the degradation of barrier function due to influenza A (H1N1) viral infection and the dose-dependent efficacy of oseltamivir (Tamiflu®) in mitigating this degradation. The tissue-electronic platform offers a non-invasive and label-free method for real-time monitoring of 3D artificial tissue barriers, without disturbing the cellular biology. It holds the potential for further applications in monitoring the structures and functions of 3D tissues and organs, significantly contributing to the advancement of personalized medicine.

Virtual impedance optimization control strategy for wind turbine grid-forming converters in weak grid

Abstract Grid forming(GFM) technology is key to the effective consumption and grid-friendly integration of large-scale wind power. However, as the new type of power system sees a high penetration of new energy sources and a decrease in circuit capacity, leading to systems exhibiting weak grid characteristics, this paper studies the adaptive characteristics of grid-forming wind turbine converters under weak grid conditions. It investigates the dynamic relationship between power coupling characteristics and the system impedance ratio based on the virtual impedance method and proposes an impedance optimization method to improve the power decoupling accuracy and dynamic response capability of grid-forming converters. The research results show that the grid-forming controller can operate stably under weak grid conditions, an increase in grid impedance ratio leads to stronger power coupling intensity, and the voltage support capability of grid-forming converters weakens. The simulation verifies that the virtual impedance optimization strategy proposed in this paper can effectively improve the power coupling characteristics of the grid-forming converter and enhance the converter’s dynamic response performance under weak grid conditions.

A Novel Repeat PI Decoupling Control Strategy with Linear Active Disturbance Rejection for a Three-Level Neutral-Point-Clamped Active Power Filter with an LCL Filter

The three-level neutral-point-clamped (NPC) active power filter (APF) is suitable for harmonic compensation in high voltage and large capacity applications. And, the harmonic compensation effect of APF depends on its dynamic performance and control. This paper propose a repeat proportional integral (PI) decoupling control strategy with linear active disturbance rejection (LADRC) to address the issues of detection in complex harmonic current and power supply current distortion when the nonlinear load varies. To simplify the design of LADRC, this paper adopts inverter current feedback control. Firstly, repeat control is introduced to optimize the traditional PI controller, which improves the compensation accuracy while ensuring the dynamic response capability of the control system. Then, to address the serious coupling of the system model in the d-q coordinate system, a reduced order linear active disturbance rejection (LADRC) control is introduced. The PI and linear extended state observer (LESO) control method is adopted in the outer voltage loop to maintain stable DC voltage and improve the ability to suppress voltage overshoot during grid connection. The effectiveness of this control method has been verified through MATLAB/Simlink. The results show that, compared with the repeat PI method, the control method based on repeat PI–LADRC can achieve better decoupling control, improve robustness and anti-interference ability, enhance the performance of the original system, and can significantly improve the harmonic suppression capability of the APF.

Integral global fast terminal sliding mode control for LLC converters

Abstract The complex characteristics of full-bridge LLC converters and PFM mode of operation result in a variety of operating modes and characteristic changes at different operating frequencies, thus increasing the difficulties in small signal characteristic analysis and good compensator design. In terms of control strategy, the traditional linear controller has limited response performance under various operating conditions such as input voltage ripple perturbation, input voltage mutation, and load mutation. To obtain greater transient response performance, stronger disturbance capability, and higher input stability of the system under different operating conditions, this paper proposes a new integral global sliding mode control law for LLC converters. In contrast to conventional control methods, the proposed control strategy exhibits enhanced dynamic response capabilities in the presence of load variations. It effectively mitigates jitter issues during steady-state operation, demonstrating a superior control performance.

A master-slave teleoperation system control model based on model predictive control with fast dynamic response

Robot technology is advancing with constantly expanding applications. Teleoperation system demonstrates its potential to extend human capabilities in unstructured and hazardous environments as an important branch of robotics. However, there are still issues involving insufficient position tracking accuracy and dynamic response capabilities. This article proposes a master-slave teleoperation system control model based on model predictive control (MPC) with a real-time dynamic adaptive parameter adjustment algorithm. We establish a model based on the integral barrier Lyapunov function and construct a dynamic optimization model based on MPC. Then we carry out rolling optimization and feedback correction at each moment to feed the predicted values generated by MPC back to correct the actual tracking trajectories in real-time. Additionally, we utilize an adaptive neural network and robust term compensation to adjust dynamic parameters and eliminate the effects of time delays, external disturbances, and model uncertainties. Theoretical simulation experiments are conducted on the Simulink platform, analyzing position tracking error and dynamic response capabilities of the master-slave robot respectively to verify the proposed model. The results indicate a reduction in the position tracking errors of the master with 10% and slave with 11%, while also showing an improvement in their dynamic response capabilities. The speed error ranges of the master and slave are shortened with 6% and 7% respectively.

Research on linear/nonlinear active disturbance rejection switching control of PMSM speed servo system

An improved linear/nonlinear active disturbance rejection switching control strategy is proposed to enhance the dynamic response and disturbance suppression capabilities of the permanent magnet synchronous motor (PMSM) speed servo system. Firstly, facilitated by a tracking differentiator, rapid setpoint tracking is achieved without speed overshoot, effectively addressing the inherent conflict between fast system response and overshooting. Secondly, weight factors are introduced into the control law, and a novel switching criterion based on the step function is designed, enabling the system to smoothly transition between operating modes in the presence of disturbances with varying amplitudes. This facilitates accurate estimation and swift compensation for disturbances, thereby enhancing control precision and robustness. Additionally, a frequency domain analysis of the extended state observer is conducted, leading to the formulation of a parameter tuning scheme for the controller. Lastly, simulation tests validate the feasibility and effectiveness of this strategy as a PMSM speed controller. The system exhibits superior dynamic tracking and disturbance suppression performance over a wide speed range.

Simulation of the Dynamic Responses of Layered Polymer Composites under Plate Impact Using the DSGZ Model

This paper presents a numerical study on the dynamic response and impact mitigation capabilities of layered ceramic–polymer–metal (CPM) composites under plate impact loading, focusing on the layer sequence effect. The layered structure, comprising a ceramic for hardness and thermal resistance, a polymer for energy absorption, and a metal for strength and ductility, is analyzed to evaluate its effectiveness in mitigating the impact loading. The simulations employed the VUMAT subroutine of DSGZ material models within Abaqus/Explicit to accurately represent the mechanical behavior of the polymeric materials in the composites. The VUMAT implementation incorporates the explicit time integration scheme and the implicit radial return mapping algorithm. A safe-version Newton–Raphson method is applied for numerically solving the differential equations of the J2 plastic flow theory. Analysis of the simulation results reveals that specific layer configurations significantly influence wave propagation, leading to variations in energy absorption and stress distribution within the material. Notably, certain layer sequences, such as P-C-M and C-P-M, exhibit enhanced impact mitigation with a superior ability to dissipate and redirect the impact energy. This phenomenon is tied to the interactions between the material properties of the ceramic, polymer, and metal, emphasizing the necessity of precise material characterization and enhanced understanding of the layer sequencing effect for optimizing composite designs for impact mitigation. The integration of empirical data with simulation methods provides a comprehensive framework for optimizing composite designs in high-impact scenarios. In the general fields of materials science and impact engineering, the current research offers some guidance for practical applications, underscoring the need for detailed simulations to capture the high-strain-rate dynamic responses of multilayered composites.