Dynamic Characteristics Research Articles

Closed Circuit of 3-Dimensional Polymer Powders

This article describes the most important stages of testing the levels of innovative readiness of a new machine and material construction solutions for closed loops of polymer powders in 3D additive manufacturing. The aim of this study was to indicate the state and directions of development of the technology of geometric recycling of polymer powders in 3D additive manufacturing, the need to control the geometric quality of the powders, and the quality of the design of machines for their processing in a closed circuit. The method was aimed at creating a strategy and verifying the stages of development of a new idea for stabilizing the geometric and dynamic design features of polymer powders for additive manufacturing (3D printing). Connections and relationships of the variable design features of powders, machines and devices with variable postulated states of products and processes allowed for the analysis, knowledge, description and development of variables for stabilizing geometric features of polymer powders in recycling. The results show the possibilities of supporting innovative creativity according to the adopted method, allow for determining the level of compliance of the quality of the products of the technical system, the effectiveness of preparatory processes for recycling and the non-toxicity of the products and processes of the new technology and their closed loop. The goal of developing useful design values, according to an integrated method of analysis, assessment and environmental development, was achieved, including the proposal of design features of razor blade shredders, supported by genetic algorithms, and devices for stabilizing polymer powders in additive manufacturing with recycling.

CFD-DEM simulation and experimental investigations on continuous hydraulic sampling of deep-sea polymetallic nodules

The physical sampling to grasp the distribution of deep-sea polymetallic nodules is essential for efficient mining. A hydraulic sampling scheme is proposed that balances low environmental disturbance, high sampling precision, broad coverage, and statistical data viability to address the limitations of existing methods. This paper utilizes a coupled approach of Computational Fluid Dynamics (CFD) and Discrete Element Method (DEM) to explore innovative solutions for precise and continuous hydraulic sampling of deep-sea polymetallic nodules. Focusing on the optimization of the double-row nozzle sampling mechanism design, simulations are conducted to analyze the dynamic characteristics of solid-liquid two-phase flow during the hydraulic sampling process under various nozzle configurations and operational water pressures of the pump. Laboratory experiments with the hydraulic sampling mechanism validate the accuracy of the simulation and the effectiveness of the optimization suggestions. The configuration is finalized with a front nozzle diameter of 10 × φ9 mm, a rear nozzle diameter of 10 × φ7 mm, and an initial working water pressure for the pump of 80 kPa. Subsequently, integrated driving-sampling experiments are conducted using a self-propelled tracked vehicle, validating the feasibility of the proposed configuration.

Static and dynamic characteristics of electrostatically coupled MEMS beams under asymmetric actuation conditions

Abstract Electrostatically actuated microbeams have gained prominence due to their applications as micro-actuators and ultrasensitive sensors. In this study, we investigate the dynamic behavior of electrostatically coupled microbeams, specifically focusing on the effects of asymmetric actuation conditions. Our investigation encompasses both static and dynamic characteristics of the coupled microbeam system, considering various gap ratios between the beams and the fixed electrodes. Within the coupled system, two doubly clamped microbeams are positioned between two fixed electrodes. We develop a reduced-order model (ROM) to investigate static and dynamic responses of the coupled system. To validate the ROM, we rigorously compare its results with finite element (FE) simulations. Our findings reveal that the gap ratios play a pivotal role in shaping the system’s response. Notably, first two natural frequencies are important for design of micro-resonators and they exhibit complex variation with respect to the applied DC voltage. Moreover, we explore resonance frequency tunability and investigated interplay between geometric nonlinearity and nonlinear electrostatic actuation forces. These insights contribute significantly to the efficient design of MEMS resonators.

Dynamic Behaviors of Composite-Laminated Concentrically Stiffened Rectangular Plates by Substructure Modal Synthesis

Existing numerical analysis methods often incur high computational costs when directly solving multi-degree-of-freedom systems due to computer capacity limitations. To address this issue, this paper took the composite laminated concentrically stiffened rectangular plate as the research object. The modal synthesis method was employed to reduce the degrees of freedom of the model, enabling the solution of complex dynamic characteristics. In the research process, the domain decomposition theory was used to divide the research object into plate and rib substructures. Based on the spectral Chebyshev method, the substructure dynamic model of the composite laminated rectangular plate was constructed. Subsequently, the function expressions of the complex load excitation on each substructure were derived, and the fixed modal synthesis method was introduced to construct the spectral Chebyshev reduced model of the substructures. According to the artificial spring theory equivalent, the interface force, and the whole reduction model of the composite laminated concentrically stiffened rectangular plate were obtained by assembling the substructure models. The time–domain and frequency–domain dynamic behaviors of composite laminated concentrically stiffened rectangular plates are yielded by solving the whole reduced model. The novelty of this work lies in combining the spectral Chebyshev method with the modal synthesis method. The established reduced model no longer depended on mesh quality and required fewer matrix dimensions and computation time compared to the whole model, while still achieving sufficiently accurate results. Additionally, based on the reduced model, an effective parametric study scheme was proposed to analyze the impact of dimensions and material properties of rib substructures on the dynamic behaviors of the composite laminated concentrically stiffened rectangular plate. These findings can be used to guide the optimal design of laminated concentrically stiffened rectangular plates in engineering practice.

Influence of Different Valve Openings on the Structural Dynamic Characteristics of a Multistage Pump

Influence of Different Valve Openings on the Structural Dynamic Characteristics of a Multistage Pump

TopicFM+: Boosting Accuracy and Efficiency of Topic-Assisted Feature Matching.

This study tackles image matching in difficult scenarios, such as scenes with significant variations or limited texture, with a strong emphasis on computational efficiency. Previous studies have attempted to address this challenge by encoding global scene contexts using Transformers. However, these approaches have high computational costs and may not capture sufficient high-level contextual information, such as spatial structures or semantic shapes. To overcome these limitations, we propose a novel image-matching method that leverages a topic-modeling strategy to capture high-level contexts in images. Our method represents each image as a multinomial distribution over topics, where each topic represents semantic structures. By incorporating these topics, we can effectively capture comprehensive context information and obtain discriminative and high-quality features. Notably, our coarse-level matching network enhances efficiency by employing attention layers only to fixed-sized topics and small-sized features. Finally, we design a dynamic feature refinement network for precise results at a finer matching stage. Through extensive experiments, we have demonstrated the superiority of our method in challenging scenarios. Specifically, our method ranks in the top 9% in the Image Matching Challenge 2023 without using ensemble techniques. Additionally, we achieve an approximately 50% reduction in computational costs compared to other Transformer-based methods. Code is available at https://github.com/TruongKhang/TopicFM.

Структурно-динамические особенности психических свойств и психоэмоционального состояния студентов в процессе учебной деятельности

An integral component of the learning process is knowledge acquisition control, which can have a significant impact on the psycho-emotional state of students and, as a result, on the effectiveness of their learning activities. In this regard, the ability of students to recognize and regulate their psycho-emotional state is of key importance. The purpose of the study was to identify the features of the mental properties and psycho-emotional state of students in the learning process. The objectives of the study were to analyze the dynamics of the psycho-emotional state of students in educational activities, to determine the impact of the knowledge acquisition control situation on the psycho-emotional state of students, to develop practical recommendations for developing students' ability to recognize and regulate their psycho-emotional state. The main research methods were psychological testing, observation and analysis. The study reveals the features of mental properties and dynamics of students' psychoemotional condition by gender component and in different periods of learning activity. It has been established that the situation of control of knowledge assimilation has a significant impact on students' psychoemotional condition, causing an increase in anxiety and stress. Practical recommendations for the development of students' skills of awareness and regulation of their psychoemotional condition are given.

Terahertz Crystal-Field Transitions and Quasi Ferromagnetic Magnon Excitations in a Noncollinear Magnet for Hybrid Spin-Wave Computation.

The complexity of interactions between the crystal-field and unusual noncollinear spin arrangement in nontrivial magnets demands novel tools to unravel the mystery underneath. In this work, we study such interaction dynamics of crystal-field excitations (CFE) and low-energy magnetic excitations in orthochromite TmCrO3 with controls of temperature and magnetic field using high-resolution magneto-terahertz (THz) time-domain spectroscopy. The THz energy spectrum spanning 0.5-10 meV possesses a low-frequency spin-excitation (magnon) mode and a multitude of CFE modes at 10 K, all of which uniquely embody a range of phenomena. For the magnon mode, a temperature dependence of peak frequency is induced by magnetic interactions between Tm and Cr subsystems. While a change from blue to red shift of peak frequency of this mode marks the magnetization reversal transition, the spin-reorientation temperature and change of magnetic anisotropy are depicted by different features of field and temperature dependent peak frequency dynamics. The modes corresponding to CFE are robust and laden with a multitude of submodes, which are attributes of nontrivial interactions across different transitions. These modes are suppressed only upon substitution of Tb3+ at the Tm3+ site, which suggests a dominant role of single-ion anisotropy in controlling entire THz excitation spectra. Overall, this remarkable range of phenomena seen through the unique lens of all-optical THz tools provides deeper insights into the origin of magnetic phases in systems with complex interactions between rare-earth and transition metal ions and provides a multitude of novel combinations of closely spaced modes for emerging hybrid spin-wave computation.

Droplet Impact on the Superhydrophobic Surface with Singularities.

Reducing the contact time of an impacting droplet is highly desirable in various industrial fields including anti-icing. With the straightforward upscaling advantage, singularities on superhydrophobic surfaces can induce an annular rebound with a limited reduction in contact time. To break this limitation and further reduce contact time, this study focuses on optimizing the singularity number and arrangement. The effects of the singularity number and dimensionless spacing (l* scaled by the droplet diameter) on the dynamic and contact time characteristics of a droplet impacting the superhydrophobic surface are experimentally studied under varying Weber numbers (We). The experimental results indicate that in comparison to the single singularity, two singularities with l* < 1.0 can generate two liquid rings with four lateral liquid subunits due to the impalement at the high We region. Owing to the reduced equivalent diameter of the subunit, increasing We results in a gradually decreased contact time and accordingly breaks the limitation. However, the liquid film cannot be pierced at l* > 1.0 with a limited reduction. Considering the further reducing potential at l* < 1.0, four singularities are explored without a further reduced contact time due to the formed central liquid film. Using an additional central singularity, the central liquid film is pierced promoting its annular rebound. In consequence, five singularities significantly break the limitation in contact time, particularly a 61.7% reduction to the superhydrophobic flat surface at l* < 1.0.

Study on dynamic shear characteristics of calcareous sand reinforced with rubber and geogrid

Study on dynamic shear characteristics of calcareous sand reinforced with rubber and geogrid

Integrating Evolutionary Game-Theoretical Methods and Deep Reinforcement Learning for Adaptive Strategy Optimization in User-Side Electricity Markets: A Comprehensive Review

With the rapid development of smart grids, the strategic behavior evolution in user-side electricity market transactions has become increasingly complex. To explore the dynamic evolution mechanisms in this area, this paper systematically reviews the application of evolutionary game theory in user-side electricity markets, focusing on its unique advantages in modeling multi-agent interactions and dynamic strategy optimization. While evolutionary game theory excels in explaining the formation of long-term stable strategies, it faces limitations when dealing with real-time dynamic changes and high-dimensional state spaces. Thus, this paper further investigates the integration of deep reinforcement learning, particularly the deep Q-learning network (DQN), with evolutionary game theory, aiming to enhance its adaptability in electricity market applications. The introduction of the DQN enables market participants to perform adaptive strategy optimization in rapidly changing environments, thereby more effectively responding to supply–demand fluctuations in electricity markets. Through simulations based on a multi-agent model, this study reveals the dynamic characteristics of strategy evolution under different market conditions, highlighting the changing interaction patterns among participants in complex market environments. In summary, this comprehensive review not only demonstrates the broad applicability of evolutionary game theory in user-side electricity markets but also extends its potential in real-time decision making through the integration of modern algorithms, providing new theoretical foundations and practical insights for future market optimization and policy formulation.

Dynamic Characteristics Analysis of Cylindrical Roller Bearing with Dimensional Deviations in Cage Pocket

Dynamic Characteristics Analysis of Cylindrical Roller Bearing with Dimensional Deviations in Cage Pocket

System fragility analysis of highway bridge using multi-output Gaussian process regression surrogate model

This study presents a multi-output Gaussian process regression (GPR) surrogate model for seismic-fragility analysis of bridge structural systems. Multi-output GPR can model the correlations among multiple outputs, accuracy and stability are achieved with fewer training data, which reduces the computational cost of fragility analysis. Furthermore, the explainability of the constructed surrogate model is implemented by adopting an automatic relevance-determination (ARD) kernel in the GPR. The estimated hyperparameters can provide the contribution of the uncertainty of each input parameter to the outputs. The fragility analysis using the multi-output GPR surrogate model was verified by applying it to a seismic isolation highway bridge with multiple spans and a curved geometry. The effectiveness of the multi-output GPR was demonstrated by the construction of an accurate and stable surrogate model with 46 inputs and 28 outputs. The relative contributions of the uncertainties to the structural properties and input earthquake loads could also be understood. The fragility curves, at both the component and system levels, were appropriately obtained using a sufficient number of samples in a Monte Carlo calculation. Furthermore, the failure modes were evaluated, identifying which structural components contributed to the system failure. This enabled discussions on structural system failures from the viewpoint of the structural dynamic characteristics of the bridge and earthquake-load properties.

A factor analysis based automatic optimization method of vibration sensor points for ship acoustic reconstruction

To address the challenge associated with optimizing the number and positioning of accelerometer points in ship acoustic reconstruction, factor analysis is applied to the automatic optimization of accelerometer position in this work according to the source independence of ship’s underwater radiated noise. Based on the numerical simulation method that calculates the vibration noise response under specific operation conditions, the minimum number and the optimal position of the accelerometers can be obtained through the factor analysis and multi-parameter comprehensive evaluation method. The results of acoustic reconfiguration and testing are compared using one test cabin as a validation object to verify the effectiveness of the proposed method. The results show that the automatic optimization algorithm can improve the reconstruction accuracy concerning the uniform distribution method. The error is reduced from 15.46% (1.5 dB) to 8.35% (0.8 dB), and the correlation coefficient is improved from 0.86 to 0.89. The semi-automatic optimization method is slightly lower in terms of errors and correlation coefficients compared to the automatic optimization algorithm. After increasing the number of monitoring positions for automatic optimization, the acoustic reconstruction accuracy is further improved, while the average error is reduced to 7.37% (0.7 dB) and the correlation coefficient is improved from 0.89 to 0.92. The experimental results are proximate to the conclusions of the simulation calculation. Even under the circumstance of multi-factor noise interference, the correlation coefficient of multi-position automatic optimization can still reach 0.8, in comparison with the uniform distribution method, the error is reduced from 32.49% (3.4 dB) to 25.94% (2.6 dB). The proposed method effectively captures the dynamic characteristics of complex structures and their impact on underwater radiated noise, and the integration of factor analysis can prominently enhance the computational efficiency, thereby providing technical support for engineering applications to the ship acoustic reconstruction.

Electric vehicle energy consumption prediction for unknown route types using deep neural networks by combining static and dynamic data

Accurate energy consumption prediction of electric vehicles (EVs) is crucial for drivers considering long trips. All the data should be provided beforehand to determine the energy consumption at the beginning of the trip. Although dynamic vehicle data (vehicle speed, state-of-charge, acceleration, etc.) cannot be known before the trip, factors related to the specified route (route type, elevation, average speed, weather, driving time, etc.) can be used to predict the consumed energy. These factors can be categorized as static and dynamic features, and thus, the question of how to effectively use static and dynamic data arises. This paper investigates the problem of predicting the energy consumption of an EV for a predetermined trip using a deep neural network (DNN) model that effectively uses static features along with dynamic segment features. Furthermore, we address the problem where the route types are unknown in advance. To include more information in the prediction model, we clustered the speed profiles using shape-based clustering with dynamic time warping (DTW) to predict the route type and used the cluster labels as static inputs. Real driving data collected from various drivers of a specific EV were used to train the DNN. The proposed DNN model was compared with the average energy consumption (AEC) model and five machine learning models. The results show that labels obtained from shape-based clustering improved the prediction more than feature-based cluster labels. The prediction errors were minimized with the proposed DNN model, where static features are introduced to the first and second layers twice.

Stock Price Prediction Research Based on CNN-LSTM

In recent years, neural network technology has been widely used in the field of stock forecasting, especially in the aspect of LSTM, which has become a research hotspot because of its unique advantages in dealing with time series problems in stock forecasting. By analyzing and summarizing the recent research literature based on LSTM and its extended model, this paper aims to deeply explore the effect and potential limitations of LSTM in capturing the dynamic and nonlinear characteristics of the stock market. By constructing different combination models combining LSTM and CNN, the paper uses normalization to preprocess the split data and realize the generalization prediction of stock price. Compared with the experimental results, a suitable combination model of LSTM and CNN is found. This hybrid model can significantly improve the accuracy and stability of prediction, and also shows the advantages in multi-input data processing. The results of this study not only provide a new technical means for the direction of stock prediction, but also explore a new direction for the further application of deep learning technology in the financial field.

Investigation of motion response suppression characteristics in floating platforms equipped with novel spiral side plates

AbstractTo enhance the stability of floating wind turbine platforms, this study combines the structural characteristics of helical side plates and proposes the installation of serrated helical side plates on Spar wind turbine platforms. Based on potential flow theory, the present study employs blade element momentum theory and radiation‐diffraction theory. Upon establishing a dynamic data link library, wind‐wave coupling was implemented, and the dynamic response characteristics of platforms with different helical side plates were compared. The results indicate that in the frequency domain analysis, the platform with serrated helical side plates demonstrates reduced sensitivity to waves, with a particularly notable increase in added mass in the heave direction, suggesting excellent hydrodynamic performance. In the time domain analysis, improvements in the platform's surge and heave stability performances were observed, measuring 10.99% and 10.64%, respectively, in extreme conditions. Owing to the unique features of the serrated structure, the tension in the mooring chains on the wave‐facing side is reduced, thereby effectively lowering the fatigue load on the mooring chains.

Deep spatial-temporal information fusion dynamic graph convolutional network for traffic flow prediction

Abstract Predicting traffic flow is vital for advancing intelligent transportation systems, but achieving precise forecasts remains challenging. To deeply explore the complex and diverse dynamic spatio-temporal correlation of traffic data, this study proposes a multi-information fusion spatio-temporal dynamic graph convolution model for traffic flow prediction. In the model construction, firstly, a dynamic graph generation module is designed to fully capture the complex spatial correlation of the traffic network and model the changing spatio-temporal interaction; secondly, the spatial self-attention mechanism is used to dynamically focus on the spatial relationship between different nodes, and combined with the dynamic graph convolution network to extract the spatial dynamic correlation features of the road network to study deep spatiotemporal information; Finally, existing models rarely consider other traffic information related to traffic flow, and a spatiotemporal information fusion module is designed to enhance the ability of the model to perceive information, thereby improving the performance of traffic flow prediction models. Experiments are conducted on four real traffic datasets using three performance evaluation metrics and 18 baseline models. The results show that the model has higher prediction accuracy.&amp;#xD;

SeqDance: A Protein Language Model for Representing Protein Dynamic Properties.

Proteins perform their functions by folding amino acid sequences into dynamic structural ensembles. Despite the important role of protein dynamics, their complexity and the absence of efficient representation methods have limited their integration into studies on protein function and mutation fitness, especially in deep learning applications. To address this, we present SeqDance, a protein language model designed to learn representation of protein dynamic properties directly from sequence alone. SeqDance is pre-trained on dynamic biophysical properties derived from over 30,400 molecular dynamics trajectories and 28,600 normal mode analyses. Our results show that SeqDance effectively captures local dynamic interactions, co-movement patterns, and global conformational features, even for proteins lacking homologs in the pre-training set. Additionally, we showed that SeqDance enhances the prediction of protein fitness landscapes, disorder-to-order transition binding regions, and phase-separating proteins. By learning dynamic properties from sequence, SeqDance complements conventional evolution- and static structure-based methods, offering new insights into protein behavior and function.

Application and Empirical Analysis of Random Volatility Model in Financial Markets

The Stochastic Volatility Model (SVM), as a key innovation in modern financial engineering, has profoundly changed our understanding of the volatility characteristics of financial markets. This model not only considers the volatility of asset prices as a time-varying random variable, but also simulates this uncertainty by introducing stochastic processes such as Brownian motion or more complex stochastic differential equations, thereby achieving a precise characterization of the dynamic characteristics of volatility. Compared to traditional fixed or historical average volatility assumptions, SVM can capture sudden changes in market sentiment, the impact of news events, and the influence of macroeconomic factors on volatility, providing investors with a more realistic perspective on the market. This article not only deeply analyzes the theoretical basis and construction methods of SVM, but also verifies its effectiveness and applicability in different financial market environments through empirical analysis and actual market data, providing valuable references and insights for financial practitioners, scholars, and policy makers.