Dynamic Characteristics Research Articles

Research on anti-rollover control of three-axle rescue vehicle based on active suspension and differential braking

Abstract. Due to its intricate operational environment, substantial mass, and elevated center of mass, the three-axle emergency rescue vehicle is more prone to rollover. This study aims to enhance the operational stability and mitigate rollover risks by establishing a rollover dynamic model with 11 degrees of freedom. Using the coupling characteristics of vehicle dynamics and tire force, an anti-rollover control strategy of the three-axle vehicle based on the cooperative work of differential braking and active suspension is proposed. According to the vehicle motion characteristics, the active suspension controller is built by using a fuzzy PID algorithm, and the differential braking controller is built by using the improved adaptive model predictive control algorithm. The step and fishhook tests of differential braking, active suspension control and joint control are carried out respectively. The simulation results show that the joint control strategy can effectively reduce the rollover tendency of the vehicle, and further improve the anti-rollover ability of the vehicle compared with the single system control.

Glucose Variability in People with Type 1 Diabetes: Associations with Body Weight, Body Composition, and Insulin Sensitivity.

The prevalence of overweight and obesity increases in people with type 1 diabetes (T1D). However, the impact of fat accumulation on glucose dynamics in T1D is poorly understood. We assessed continuous glucose monitoring (CGM) parameters in patients with T1D depending on their body weight, body composition, and insulin sensitivity. In 547 patients, including 238 overweight/obese individuals, CGM-derived time in range (TIR) and glucose variability (GV) were estimated. Body composition was assessed by DXA. Estimated glucose disposal rate (eGDR) was used as an indicator of insulin sensitivity. Overweight/obese patients, when compared to normal-weight ones, have a lower time below range (TBR) (<3 mmol/L), GV, and experienced fewer episodes of low glucose. In men, lower TIR, higher time above range (TAR), and GV reduction were associated with central adiposity assessed by total, trunk, and android fat mass. In women, gynoid fat mass only was associated with a lower TIR and higher TAR. The eGDR was a positive predictor of TIR and a negative predictor of TAR, TBR, and GV in men and women. In conclusion, adiposity in people with T1D is associated with a lower risk of CGM-confirmed hypoglycemia, higher TAR, and reduced GV. These features of daily glucose dynamics may be mediated by insulin resistance.

Investigating the role of solvents in modulating glass transition and dynamic mechanical characteristics of polystyrene film

This study investigates how different solvents impact the glass transition and dynamic mechanical properties of polystyrene (PS) films. Using FTIR spectroscopy, it analyzes solvent-induced variations in molecular vibrations, particularly in the phenyl ring region. Results show solvent-specific effects on the 750 cm-1 peak, associated with the "meta-substituted aromatic ring," influencing phenyl ring vibrations. DSC analysis highlights solvent effects on the glass transition temperature (Tg), revealing complexities in PS films prepared with chloroform, tetrahydrofuran (THF), and toluene. Varied Tg behavior suggests solvent-driven structural modifications and gradual solvent removal. Dynamic Mechanical Analysis (DMA) demonstrates solvent-dependent changes in storage modulus, loss modulus, and tan δ, indicating solvent polarity role in polymer chain dynamics. Toluene-induced plasticization reduces Tg and increases creep compliance, revealing PS film sensitivity to solvents. These findings stress solvent selection importance in tailoring thermal and mechanical properties of polymer films for specific applications.

ORB Visual and WiFi Online RSSI fusion SLAM

Simultaneous Localization and Mapping (SLAM) technologies are indispensable for indoor service robots, enabling them to navigate through and interact with environments. Visual SLAM systems often encounter significant challenges such as dynamic obstacles, variable lighting, feature scarcity, and perceptual aliasing in real-world scenarios. By merging the precise environmental mapping capabilities of visual SLAM with the ubiquity and stability of WiFi signals, our method effectively addresses the limitations typically associated with visual SLAM. Notably, our fusion technique leverages existing WiFi infrastructure, thus providing a cost-effective improvement in spatial awareness without the extensive offline database requirements of WiFi RSSI-based localization. Comparative performance evaluations highlight that our graph optimization-based approach not only surpasses the original ORBSLAM3 method but also significantly outperforms the Extended Kalman Filter (EKF) in terms of accuracy, particularly in environments characterized by poor lighting, feature-less scenes, and significant occlusions. This is evidenced by a reduced Root Mean Square Error (RMSE) in localization: 3.09m for our method versus 4.02m for EKF. This enhancement in precision underscores the potential of our integrated system to advance indoor navigation technologies, making it a crucial development in the field of robotics and automated systems.

Comprehensive Analysis and Development of Electric-Drive-Wheel with Idler Gear

This paper provides a comprehensive analysis of the electric-drive-wheel (EDW) with idler gear. From the perspective of automotive engineering, the EDW is an integrated execution unit that combines the function of powertrain and suspension. As a result, the research on EDW involves the intersection of multiple disciplines. The evolution of idler gear configuration requires some geometric constraints. Under this premise, the longitudinal and vertical dynamic characteristics of the system were studied, respectively. There are several factors that influence the system performance, such as the gear parameters, the position of the electric motor, and the suspension K&amp;C value. The principles of each parameter on the output indicators were studied, with an optimized plan and simulation comparison to check the correctness of theory. In the end, the prototype was equipped with a test bench, covering a wide range of working operations to examine the expected performance of EDW design. The step response test of the EDW result showed a balanced performance in transmission smoothness and responsiveness agility.

Numerical study of steady-state dynamic characteristic of non-pneumatic tire with local structural damage

Non-pneumatic tires (NPTs) fundamentally avoid the risk of tire blowout of traditional pneumatic tires, but the overall and key component performance inevitably degrades due to factors such as high temperature, variable load, variable working conditions and impact in its service process. Once this leads to failure, it will significantly impact on vehicle safety. To this end, this paper studied the influence of local structural damage on the dynamic response of NPTs, which lays the foundation for realizing health monitoring of intelligent non-pneumatic tires (INPTs). Firstly, a three-dimensional nonlinear finite element model of NPTs was established, and the failure locations of NPTs were determined by fracture mechanics and maximum strain energy density; Secondly, the influence of structural damage on the static and dynamic performance of NPTs was analyzed; Finally, the sensitivity of the acceleration signal and sensor position of the tire inner liner to the local structural damage was studied. The research results show that structural damage will cause the stress of the spokes and shear layer to increase, and as the number of broken spokes increases, the shear layer will bear a larger load. Compared with the circumferential and lateral acceleration, the radial acceleration has the highest sensitivity to the damage of NPTs. The sensor closest to the damage location is the most sensitive to the damage. The research results provide a reference for the structural optimization design and health monitoring of INPTs.

Dynamics of an offshore drilling tube system based on a riser–drill string–drilling fluid coupling model

ABSTRACT Accurate prediction of dynamic characteristics of an offshore drilling tube system under marine environmental loads is significant to secure the safety of offshore drilling operations. Based on previous works for coupling riser with drill string to form a pipe-in-pipe structure, in this present work, the influence of drilling fluid is taken into consideration to develop a riser–drill string–drilling fluid coupling model; and the corresponding verifications are conducted via comparison with the experimental results. The collisions between riser and drill string are demonstrated to restrict the lateral drifting of the riser, which, however, can be intensified by the upward flow of drilling fluid. In addition, as increasing the offset of the drilling platform, the lateral drifting of the riser also increases; moreover, the position for the maximal drifting moves upwards to the sea surface. When the part of the drill string below the mudline is taken into consideration, the increasing angle of well inclination is illustrated to restrict the lateral drifting of the riser above the mudline.

Nonlinear interactions of an n-layer X-shape low-frequency vibration isolator equipped with a nonlinear vibration absorber at 1:1 internal resonance: analytical and numerical investigations

This work addresses, for the first time, the nonlinear dynamics of an n− layers bioinspired X− shape low-frequency vibration isolation system equipped with a nonlinear vibration absorber. The comprehensive mathematical model governing the two-degree-of-freedom system has been derived using Lagrangian mechanics. The method of averaging was applied to obtain an accurate analytical solution for the derived mathematical model. Utilizing the established analytical solution, the dynamical characteristics of the X− shape vibration isolator have been explored both as a single-degree-of-freedom system and after coupling with the proposed vibration absorber, as a two-degree-of-freedom system. The influence of different system parameters, including the number of structure layers, the initial inclination angle, the X− shape rod length, and the absorber stiffness and damping coefficients, on the vibration isolation performance has been investigated. The main findings indicate that while the single-degree-of-freedom X− shape low-frequency vibration isolator performs well in eliminating low-frequency vibration excitations, it exhibits strong vibration levels when the base excitation frequency exceeds the structure's natural frequency. Additionally, it was found that coupling a highly damped linear vibration absorber to the X− shape isolator not only eliminates the strong oscillations of the structure when subjected to high-frequency base excitations but also enhances the structure's low-frequency vibration isolation performance. Moreover, it was reported that integrating a nonlinear vibration absorber with either soft or hard spring characteristics instead of a linear one may degrade the vibration isolation performance of the structure rather than enhance it. Finally, numerical validation of all the analytical results was performed, which showed excellent correspondence with the analytical findings.

Research on dynamic constraint mechanism and motion control of intelligent vehicles based on preview distance optimization under complex road conditions

Aiming at the coupling and conflict issues between intelligent vehicle dynamics characteristics and path tracking system under complex road conditions, this paper investigates the dynamics constraint mechanism and motion control of intelligent vehicles, and proposes an intelligent vehicle lateral–vertical cooperative control method based on optimized preview distance. To begin with, an intelligent vehicle path tracking control system based on expected yaw velocity has been designed by establishing a three-degree-of-freedom dynamic model for intelligent vehicle. Then, we analyzed the mechanism by which changes in vehicle speed, road curvature, and preview distance affect the accuracy of vehicle path tracking and handling stability. Considering the “human-vehicle-road” system in intelligent transportation systems, critical values for collision and instability were set. Furthermore, we designed a proactive optimization method for preview distance under different working conditions, using an optimization algorithm to improve path tracking accuracy while ensuring vehicle stability, based on the lateral displacement deviation and lateral orientation deviation representing the accuracy of path tracking, as well as the lateral acceleration representing handling stability. Finally, hardware-in-the-loop platform test was conducted. The simulation and test results show that the optimized path tracking algorithm reduces lateral deviation to as low as 0.05 m, and the stability constraint control in the algorithm can be triggered promptly even under extreme conditions.

Comparative study of adaptive trajectory tracking controller for four-wheel mobile robot with prescribed-prediction performance

The nonlinear external disturbances and unmodeled dynamics characteristics have crucial impacts on trajectory tracking control accuracy of a four-wheel mobile robot (FWMR) under complicated working conditions. In this work, an adaptive trajectory tracking controller is designed for the FWMR to achieve the prescribed-prediction performance. On the basis of establishing the FWMR’s dynamics equations, an enhanced prescribed performance function (EPPF) is constructed to restrain the tracking errors of the FWMR within a certain range without requiring the exact initial conditions, thus guaranteeing the transient performance of the control system. Then, an optimal-predictive control (OPC) approach is presented to fulfill the asymptotic stability of the tracking errors of the FWMR. Specifically, the radial basis function neural network (RBFNN) incorporating a minimum parameter learning approach that are implanted into the expected controller is designed to attenuate the nonlinear external disturbances and the unmodeled dynamics of the FWMR. Lastly, comparative simulation investigations are carried out to illustrate the superiority of the proposed EPPF-OPC controller, and moreover, the comparative experiments are further performed to validate the practical effectiveness of the EPPF-OPC controller based on a self-established robot operating system (ROS) test platform of the FWMR.

Effect of relative phase of loose tie rods on nonlinear dynamic behavior of rod-fastening rotor-bearing-seal system

Loosening of multiple tie rods in a circumferential rod-fastening rotor caused by thermal deformation and centrifugal loads is very common in engineering. The relative position of loose tie rods in the circumferential direction will also have an important impact on rotor dynamics. The relevant research has not been done yet. In this paper, a contact model of the rough machined surface is firstly established by combining the probability functions describing rough surfaces and fractal contact theory of asperities, then the contact stiffness model between disks is established based on the contact model. Finally, the dynamic model of the circumferential rod-fastening rotor-bearing-seal system is established and solved. The effect of relative phase of loose tie rods on the nonlinear dynamic characteristics of rod-fastening rotor-bearing-sealing system is studied. It can be concluded that relative phase of the loose tie rod has an important influence on the frequency, bifurcation, and periodic motion of the rotor system. At lower speed, the anisotropy of contact stiffness caused by loose tie rods has little effect on the rotor trajectory, while the generalized bending moment has a greater influence on dynamics of the system. At higher speed, the anisotropy of contact stiffness has a great influence on the axis rail of the rod-fastening rotor.

Dynamic features of virus protein 1 and substitutions in the 3-phenyl ring determine the potency and broad-spectrum activity of capsid-binding pyrazolo[3,4-d]pyrimidines against rhinoviruses

Pyrazolo[3,4-d]pyrimidines represent one potent class of well tolerated and highly active rhinovirus (RV) inhibitors that act as capsid binders. The lead compound OBR-5-340 inhibits a broad-spectrum of RVs. Aiming to improve lead activity, we evaluated the impact of structural modifications in the 3-phenyl ring of OBR-5-340 on its potency and spectrum of anti-RV activity vitro. Our results demonstrate the crucial role of substitution at position 4 for strong, broad-spectrum anti-RV activity. The 4-methyl (RCB23137) and 4-chloro (RCB23138) derivatives outperformed OBR-5-340 in terms of potency and anti-RV activity spectrum. Based on these findings, the compounds were selected for computational binding studies. Molecular dynamic simulations with six RVs differing in OBR-5-340, RCB23137, and RCB23138 sensitivity proved the impact of dynamic features of two VP1 loops enveloping these inhibitors on antiviral potency.

Hybrid network via key feature fusion for image restoration

In the field of artificial intelligence, combining transformers and convolutional neural networks (CNNs) to improve performance has become a popular solution for various image restoration tasks. However, the hyperparameters related to feature levels are empirical, leading to the inevitable presence of redundant features that hinder effective image restoration. Additionally, the current method of fusing global and local information is simple and direct, failing to fully exploit the potential of hybrid architectures. To address this issue, we propose a key feature fusion hybrid network (KF2H-Net) that reduces redundancy and dynamically fuses key features. On one hand, we create different learnable selection mechanisms within the hybrid network’s various units to choose global key features and local key features, enhancing the depth perception and selection capabilities for different features. On the other hand, through a parameter fusion module for dynamic feature fusion, we refine the multi-feature fusion method to emphasize the more critical features for image restoration. In order to verify the general performance of the proposed KF2H-Net, we specially selected three typical scenarios (underwater, low-light, and haze) for testing. KF2H-Net represents a novel approach to hybrid models addressing practical applications in the field of artificial intelligence. Extensive experiments show that KF2H-Net achieves state-of-the-art performance across different scenarios.

Ratio of Stem Wood Volumes in Forest Stands and Dead Wood of the Taiga Spruce Forests in European Russia

The article discusses the formation of the stem wood volumes ratio between the forest stands and the deadwood in an indigenous forest community with a contribution from wood-decaying fungi, using the spruce formations of the European Russia’s taiga zone as an example. For the analysis, 30 spruce ecosystems were taken, 10 in each of the taiga subzones (northern, middle and southern) with different dynamic characteristics — climax, demutational and digressional ecosystems. Using the method of study plots (SP), the silvicultural characteristics of ecosystems and the morphometric indicators of trees were described, and the volumes of stem wood of the forest stands forming phytocenoses and the deadwood decomposed by wood-destroying fungi were calculated. Indicators of the stem wood volumes ratio between the forest stands and the dead wood in spruce plantations of various dynamic phases were calculated, for which phase position scores were calculated. Indicators of the relationship between the volumes of stem wood in forest stands and in dead wood (R2)and linear correlation coefficients (r) for forest stands of different successional positions were determined in Microsoft Excel. The calculations confirmed the presence of a relationship between the stem wood volumes of tree stands and deadwood for the studied spruce forests using a point estimate of the phase position of ecosystems: in both programs (R2 and r) the relationship indicators were characterized as strong to very strong (Chaddock, 1925).

A Grouping and Aggregation Modeling Method of Induction Motors for Transient Voltage Stability Analysis

Induction motors are the most common type of motor in power systems, constituting approximately 70–90% of the dynamic loads, making them significant contributors to system dynamics. In transient voltage stability analysis, dynamic equivalent models are commonly used to simplify the representation of a group of induction motors. This paper presents a method for the grouping and aggregation of induction motors at a common bus. Firstly, grouping rules are provided for clustering induction motors into several subgroups based on the mechanical principles of rotor force and motion, and aggregation rules are provided for aggregating a motor subgroup into a single-unit model based on the relationship between voltage drop and power transmission in distribution networks. Secondly, guided by the grouping rules, high-speed remaining electromagnetic torque and low-speed remaining electromagnetic torque are defined as new clustering indicators, and an adaptive K-means clustering method using silhouette coefficient verification is introduced to obtain the optimal motor subgroups. Thirdly, guided by the aggregation rules, a dynamic equivalent method is further introduced to obtain the equivalent single-unit model from a motor subgroup. Lastly, a transient voltage stability simulation in a typical distribution network is presented to illustrate that the proposed clustering and equivalent methods are more reasonable, accurate, and effective than traditional methods, as the obtained model has better dynamic characteristics and can more accurately reproduce the process of voltage collapse.

A Hybrid Segmentation Algorithm for Rheumatoid Arthritis Diagnosis Using X-ray Images

Rheumatoid Arthritis (RA) is a chronic autoimmune illness that occurs in the joints, resulting in inflammation, pain, and stiffness. X-ray examination is one of the most common diagnostic procedures for RA, but manual X-ray image analysis has limitations because it is a time-consuming procedure and is prone to errors. A specific algorithm aims to a lay stable and accurate segmenting of carpal bones from hand bone images, which is vitally important for identifying rheumatoid arthritis. The algorithm demonstrates several stages, starting with Carpal bone Region of Interest (CROI) specification, dynamic thresholding, and Gray Level Co-occurrence Matrix (GLCM) application for texture analysis. To get the clear edges of the image, the component is first converted to the greyscale function and thresholding is carried out to separate the hand from the background. The pad region is identified to obtain the contours of it, and the CROI is defined by the bounding box of the largest contour. The threshold value used in the CROI method is given a dynamic feature that can separate the carpal bones from the surrounding tissue. Then the GLCM texture analysis is carried out, calculating the number of pixel neighbors, with the specific intensity and neighbor relations of the pixels. The resulting feature matrix is then employed to extract features such as contrast and energy, which are later used to categorize the images of the affected carpal bone into inflamed and normal. The proposed technique is tested on a rheumatoid arthritis image dataset, and the results show its contribution to diagnosis of the disease. The algorithm efficiently divides carpal bones and extracts the signature parameters that are critical for correct classification of the inflammation in the cartilage images.

Dynamic Response and Rock Damage of Different Shapes of Cavities under Blasting Loads

In order to investigate the dynamic response and rock mass damage characteristics of cavities with different shapes under blasting loads, this paper, through a combination of model tests and numerical simulations, studies the stress distribution, strain, failure modes, and blasting fragment size distribution of cavities with different shapes subjected to blasting loads. The results show that under the action of blasting loads, the presence of cavities with different shapes significantly affects the blasting effects and rock mass damage. Spherical cavities exhibit excellent blast resistance, whereas rectangular and triangular cavities are prone to stress concentration at their tips, which in turn promotes rock mass damage and failure. Subsequent analysis of the blasting fragment sizes reveals that rectangular and triangular cavities yield more favorable blasting results than spherical cavities. The research findings provide important theoretical foundations and practical guidance for the design and construction of underground engineering blasting, contributing to enhancing engineering safety and promoting the sustainable development of the underground engineering industry.

Experimental study on deformation characteristics of new prestressed subgrade under static and dynamic loads

An in-deep comprehension of the static and dynamic operational characteristics of prestressed subgrade is essential for its analysis, design, and service performance evaluation. Based on the Buckingham π theory, a novel scaled static and dynamic model test system of the prestressed subgrade has been developed. The structural components, functional characteristics, and working mechanism of the test system were comprehensively elucidated, and a suite of static and dynamic model tests was conducted to study the deformation characteristics of the prestressed subgrade. It is demonstrated that the prestressed steel bars underwent prestress loss due to the additional stress induced creep of the soil elements below and adjacent to the load transfer plates (LTPs). Therefore, it is advisable to over-tension the prestressed steel bars in practical engineering. Upon the application of prestress, the subgrade surface experienced slight uplift deformation, which did not change the geometric shape and smoothness of the subgrade surface and demonstrated that the prestress reinforcement effect could diffuse to the subgrade surface. In the static double-load-plates tests, the prestressed subgrade presented obvious advantages in controlling the subgrade surface settlement and slope lateral deformation compared to the unreinforced subgrade, which could therefore improve the deformation resistance of the subgrade. In the short-term dynamic loading tests, both the acceleration and dynamic displacement of the subgrade approximately linearly decreased with an increase in the prestress, implying that the horizontal prestress had a notable beneficial impact on mitigating the subgrade vibration. Additionally, with the long-term dynamic loading, the prestress reinforcements could significantly restrain the cumulative plastic deformation of the subgrade, with the cumulative deformation decreasing as the applied prestress increased. The developed test system offers viable and implementable technical means for investigating the enhancement mechanism of a prestress reinforced subgrade, and the insights gained from the tests contribute to elucidating the effect of prestress reinforcements on the subgrade’s static and dynamic performance.

Predicting social media users' indirect aggression through pre-trained models.

Indirect aggression has become a prevalent phenomenon that erodes the social media environment. Due to the expense and the difficulty in determining objectively what constitutes indirect aggression, the traditional self-reporting questionnaire is hard to be employed in the current cyber area. In this study, we present a model for predicting indirect aggression online based on pre-trained models. Building on Weibo users' social media activities, we constructed basic, dynamic, and content features and classified indirect aggression into three subtypes: social exclusion, malicious humour, and guilt induction. We then built the prediction model by combining it with large-scale pre-trained models. The empirical evidence shows that this prediction model (ERNIE) outperforms the pre-trained models and predicts indirect aggression online much better than the models without extra pre-trained information. This study offers a practical model to predict users' indirect aggression. Furthermore, this work contributes to a better understanding of indirect aggression behaviors and can support social media platforms' organization and management.

Hotel demand forecasting with multi-scale spatiotemporal features

Accurate demand forecasting is critical to hotel revenue management and related decision-making. Considering the heterogeneity and dynamics of spatial effects across different time scales, this study introduces a novel model which can deeply extract these features to improve the forecasting performance of hotel demand. Specifically, the model constructs input variables with different periodicities and then integrates a Transformer neural network and long short-term memory to extract multi-scale and dynamic spatiotemporal features to generate accurate forecasts. The effectiveness of the model is verified through an empirical case in Xiamen, China. Results suggest our model significantly outperforms benchmarks in terms of accuracy and robustness. The findings extend the application of spatial-temporal modeling in hotel demand forecasting. Hotel managers can use our forecasts to optimize operations, improve revenues, and control risks. The extracted spatiotemporal features can also help managers examine cooperation and competition relationships with neighbor hotels.