Typical Nonlinear Characteristics Research Articles

Structural design and control strategy of a cable-driven robot under high-altitude facade conditions with large span and multiple constraints

Purpose This paper explores the challenges of using cable-driven parallel robots on high-altitude, large-span facades, where redundancy in multicable systems and the elastic deformation of the cables are significant issues. This study aims to improve the accuracy and stability of the work platform through enhanced control strategies. These strategies address the redundancy in multicable systems and reduce the risks associated with cable deformation and mechanical failures during large-span movements. Design/methodology/approach The paper proposes a dynamic model for a four-rope parallel robot designed explicitly for large-span applications. The study introduces a position–force control strategy incorporating kinematic inverse solutions and a rope dynamics model to account for rope elasticity and its effects. This approach increases the number of system equations to match the unknowns, effectively solving the redundancy problem inherent in multicable systems. In addition, the tension changes of ropes and the stability of the working platform are examined under different motion distances (X = 50 m and X = 100 m) and varying Young’s modulus values (K = 5000 MPa and K = 8000 MPa). Findings This study’s large-span rope force–position control strategy successfully resolves the typical nonlinear characteristics and external disturbances in multicable parallel systems. By continuously monitoring and adjusting cable tension and end positions, this strategy ensures precise control over each cable’s tension, optimizes the distribution of cable tensions and maintains the system’s stability and response speed. The analysis in this paper indicates that this control strategy significantly improves the motion accuracy of robots operating on large-span high-altitude facades. Practical implications Industry adoption: The design and control strategies developed for the four-cable-driven parallel robot can be adopted by companies specializing in facade maintenance, construction or inspection. This could lead to safer, more efficient and cost-effective operations, especially in challenging environments like high-rise buildings. Innovation in robotic solutions: The research can inspire innovation within the field of robotics, particularly in developing robots for specific applications such as large surface maintenance. It showcases how adaptive control and stability can be achieved in complex operational scenarios. Safety improvements: By demonstrating a more stable and precise control mechanism for navigating large facades, the study could contribute to significant safety improvements, reducing the risk of accidents associated with manual facade maintenance and inspection tasks. Originality/value This paper combines the force/position hybrid control method with actual robotic applications, offering a novel solution to the complex issue of controlling cable-driven parallel robots in challenging environments. Thus, it contributes to the field. The proposed method significantly enhances the precision and stability of such systems and provides robust technical support for high-precision tasks in complex mechanical settings.

Research on movement of dimensional singularities without elastic interaction in gasketed joints

Loss of clamping force due to elastic interaction is unpredictable and nonnegligible with the typical non-linear characteristics of gasketed joints. It is hard to weaken or eliminate elastic interaction by suitable joint dimensions. An analytical model of elastic interaction with dimensional representation is constructed using gasket deformation as a bridge. The influence of multi-dimensional dimensions on clamping force losses, such as bolt spacing, joint width and thickness, was explored by non-linear finite element analysis for a typical sandwich structure with two-bolt gasketed joints. Dimensional singularities without elastic interaction are validated and their approximate range is determined. It is clarified that initial clamping force or joint material can cause dimensional singularity movement. This paper provides a theoretical basis for solving the problem of clamping force losses and inhomogeneities effected by elastic interactions in engineering.

Research on the moderate range of enterprise financialization and R&D innovation from a nonlinear perspective

<abstract> <p>The impact of financialization on R&D innovation in enterprises exhibits typical nonlinear dynamic characteristics. Based on this, in this study we constructed a threshold regression model to test the moderate range that can maintain the positive relationship between financialization and R&D innovation of enterprises, and also analyzed the nonlinear influencing mechanism by setting the interaction term. Considering the differences in factor intensity of various industries, in this research we also tested the heterogeneity in the moderate range among different industries. Using annual data of Chinese listed companies from 2010 to 2019, the following empirical findings were obtained: First, the impact of enterprise financialization on R&D innovation presents a nonlinear feature, i.e., a typical threshold feature. The level of enterprise financialization first positively affects the R&D innovation of enterprises, and after it reaches a certain turning point, the financialization behavior negatively affects the R&D innovation of enterprises. Second, the nonlinear feature of the mechanism by which enterprise financialization affects R&D innovation is reflected in the substitute effect, and there are disparities among different financialization levels. Third, industries with varying levels of factor intensity not only demonstrate differences in moderate intervals, but also exhibit significant disparities in the degree of impact exerted by financialization on innovation.</p> </abstract>

Dynamic adaptive control of boiler combustion based on improved GNG algorithm

The boiler combustion process contains complex physicochemical changes, which is a nonlinear time-varying industrial process with strong interference and multivariate strong coupling. For this kind of boiler combustion process with typical nonlinear characteristics and complex mechanism, it is difficult to establish an accurate mechanism model by conventional modeling methods, so it is difficult to meet the new requirements for the optimal control of the boiler. The large amount of data accumulated in the operation process contains rich system information, and the data-driven modeling and control method provides an effective way for the operation optimization of the unit. Data dynamic characterization and control technology is an important means of data mining, coal-fired boiler data has obvious temporal and drift characteristics, for the current data tracking and supervision algorithms mostly lack of dynamics, real-time and stability issues, design an adaptive clustering model based on the improved growth of neural gas model (GNG), the establishment of nodes based on the probability, the range of the search, the average distance of node A node generation and deletion mechanism based on probability, range search and average distance of nodes is established to realize real-time monitoring of drift data. Finally, the experiments are carried out by analyzing the dynamic data of coal-fired boiler, and the experimental results show that the model and algorithm have stronger real-time tracking ability for dynamic drift data, and can accurately and effectively monitor and control the dynamic data of coal-fired boiler.

Residual GCB-Net: Residual Graph Convolutional Broad Network on Emotion Recognition

Electroencephalogram (EEG) data is commonly applied in the emotion recognition research area. It can accurately reflect the emotional changes of the human body by applying graphical-based algorithms or models. EEG signals are non-linear signals. Biological tissues’ adjustment and adaptive ability will inevitably affect electrophysiological signals, making EEG have the typical non-linear characteristics. Graph Convolutional Broad Network (GCB-net) extracted features from non-linear signals and abstract features via stacked CNN. It adopted the broad concept and enhanced the feature by the broad learning system (BLS), obtaining sound results. However, it performed poorly with increasing network depth, and the accuracy of some features decreased with BLS. This paper proposed a Residual Graph Convolutional Broad Network (Residual GCB-net), which promotes the performance on a deeper-layer network and extracts higher-level information. It substitutes the original convolutional layer with residual learning blocks, which solves the deep learning network degradation and extracts more features in deeper networks. In SEED data set, GCB-Res net could obtain the best accuracy (94.56%) on the all-frequency band of differential entropy (DE) and promote much on another feature. In Dreamer, it obtained the best accuracy (91.55%) on the dimension of Arousal. The result demonstrated the excellent classification performance of Residual GCB-net in EEG emotion recognition.

Dynamic characteristics of carbonate saline soil under freeze–thaw cycles in the seaonal frozen soil region

The occurrence of road damage, such as frost heave and thaw settlement, is highly likely in saline soil subgrades in seasonally frozen areas due to traffic loads and freeze–thaw cycles (FTC). In this regard, a low-temperature dynamic cyclic triaxial test was conducted to investigate the dynamic characteristics of carbonate saline soil under traffic load. The study explores the impact of salt content (SC) and the number of FTC on the backbone curves, the dynamic shear modulus (TDSM), and the damping ratio (TDR) characteristics of saline soil. An improved H-D model was employed to derive the dynamic backbone curve, and TDSM and TDR empirical formulas were established.The study's results show that the backbone curves of saline soil exhibit typical nonlinear and strain-hardening characteristics. Additionally, a higher salt content or more FTC result in a smaller TDSM and a larger TDR of the remodeled saline soil. TDSM shows an inverted “S” shape with an increase in cyclic shear strain, and the decay rate of TDSM increases when the cyclic shear strain is greater than 10-4 and less than 10-3. The decay rate of TDSM decreases and approaches a constant value after the cyclic shear strain exceeds 10-3. The decay of TDSM of low-salt soils (salt content less than 0.5%) mainly occurs in the first six freeze–thaw cycles. In contrast, the decay of TDSM of high-salt soils (salt content more than 0.5%) remains highly dependent on the number of FTC. Furthermore, TDR show an flat “S” shape with an increase of cyclic shear strain. The rate of increase in TDR of low-salt soil decelerates after six FTC, while the rate of increase in TDR of high-salt soil decelerates after nine FTC. Finally, empirical formulas for TDSM and TDR were established by varying the fitting parameters. The results of this study provide a useful reference for engineering projects in regions that encounter seasonal freezing. These findings will assist engineers in the design and construction of structures that can withstand the detrimental effects of traffic loads and FTC.

MPPT study of PV cells in complex environments

In operation, the solar power generating system’s output power from the photovoltaic array is sensitive to external conditions including light intensity and temperature, and displays typical non-linear characteristics. Hence, monitoring the maximum output power of PV cells in real time is necessary to guarantee that the maximum power output of PV cells is constantly maintained, enhancing the efficiency with which PV cells are used. Therefore, this paper is based on the immune algorithm, simulates that the biological immune system will be affected by the antigen infestation and produce resistance to carry out antigen recognition, memory, and other immune response reactions through MATLAB / Simulink simulation, and adopts the traditional perturbation observation method. The results of the simulation show that the Composite algorithm has the benefits of high tracking accuracy, fast tracking speed, stable tracking, and low false positives.

Receptance coupling substructure analysis based FRF modeling for multi-segment shell with nonlinear joint interface

Multi-segment shell typical of underwater vehicles is currently important underwater equipment. Stealth performance is a key indicator to improve the working capacity of underwater vehicles. Low-noise design is an important means to improve stealthy performance. Accurate and effective identification of its structure Frequency Response Function (FRF) is the premise of low-noise design. However, the multi-section shell joint interface has many connection points, complex transmission path and typical nonlinear characteristics, which challenges the acquisition of its structural FRF. The Receptance Coupling Substructure Analysis (RCSA) is used to segment the multi-stage shell, which can realize the integration of finite element method and experimental method, and provide an effective way to obtain the FRF of multi-segment shell. At the same time, the multi-segment shell is a typical thin-wall structure, and its actual buckling process under impact is a nonlinear (large deformation) process. Therefore, the nonlinear joint interface is constructed based on linear connection, and the RCSA for nonlinear joint interface is studied. On the basis of accurately obtaining the FRF, it is of theoretical and application significance to study the low noise design of shell.

Numerical and experimental investigation of longitudinal rail creep at turnouts on steep ramps under repeated loads considering realistic braking loads of vehicles

Turnouts on steep ramps are subject to both temperature loads and frequent vehicle braking loads, which may lead to an accumulation of excessive residual longitudinal rail displacement, thereby leading to turnout service failure and threatening vehicle operation safety. In this paper, the following research work was carried out to study the longitudinal rail creep at turnouts on steep ramps: Experiments were conducted to investigate the longitudinal resistance characteristics of fasteners under repeated loading, and based on the experiment results, an MBS-FE simulation model was established for the longitudinal rail creep at turnouts on steep ramps, with real vehicle braking loads and fastener resistance degradation taken into consideration in the model. The change law for longitudinal displacement of turnout rails caused by temperature load and frequent vehicle braking load was studied based on the model. A full-scale experiment was then conducted on the longitudinal rail creep at turnouts on steep ramps under repeated loading conditions in order to verify the results of the simulation. A comprehensive and in-depth study of the longitudinal rail creep at turnouts on steep ramps impacted by repeated loading was conducted through the combination of theoretical analysis and experiment. The following conclusions were drawn: The longitudinal resistance of fasteners displays typical nonlinear characteristics and degrades under repeated loading, these characteristics lead to an obvious accumulation of residual longitudinal displacement of turnout rails on steep ramps under repeated loading, and the increment of residual longitudinal displacement of turnout rails reaches a maximum after the first loading–unloading cycle, and with the increase of the number of load action, the accumulated longitudinal residual displacement of turnout rails increases rapidly in the early stage, and then the growth gradually slows down and eventually stabilizes. The simulation results were found to be very similar to the full-scale experiment results, verifying the accuracy of the analytical model established in this paper.

TSCK guided parameter convex optimization tunable Q-factor wavelet transform and its application in wheelset bearing fault diagnosis

Wheelset bearing is a typical vulnerable structural component in high-speed trains and heavy haul vehicles. In addition to the typical nonlinear and nonstationary characteristics, the vibration signal of wheelset bearing also contains track subgrade vibration and transmission path coupling interference components. To solve this problem, this paper proposes a new feature extraction method for wheelset bearing faults. This method constructs the Teager energy spectrum correlation kurtosis, which is purposely sensitive to periodic fault impulse components, as the objective function. The Q-factor and redundancy of tunable Q-factor wavelet transform are selected by using the parameter convex optimization method, which makes the signal decomposition have better sparsity, so as to extract fault information accurately. Simulated analysis, experimental signal analysis of QPZZ-II test-bed, and experimental signal analysis of wheelset bearing test-bed show that the proposed method can suppress the influence of nonperiodic transient impulse components, harmonic components, and noise components in the signal and accurately extract the periodic impact characteristics of bearings.

Nonlinear Resonance Characteristics Analysis of DBD Load Based on State Plane Trajectory

Dielectric barrier discharge (DBD) loads and driving converters are widely utilized in low-temperature plasma generation, but their resonant tanks have typical nonlinear characteristics with control issues. The state plane trajectory analysis is used in this article to investigate the resonant behavior of a DBD load. The converter's working principle and state plane modeling are first discussed. The impact of a high-ratio step-up transformer with parasitic capacitance is then explored, and the updated trajectory model is examined. The DBD platform is created to verify the suggested model, and the associated experimental data are provided. The results of the experiment demonstrate that the discharge trajectory is a zone with distinct borders. The trajectory equations can be utilized to calculate the load equivalent capacitance, and the difference in the boundary equivalent capacitance represents the discharge intensity. The pattern of resonant frequency variation with discharge power is given. The impact mechanism of discharge phenomena and resonance characteristics is discussed.

Characteristics of dynamic shear modulus and damping ratio and the structural formula of EPS particles lightweight soil

Dynamic triaxial tests of lightweight soil were conducted to explore the dynamic characteristics of expanded polystyrene (EPS) particle lightweight soil under traffic load. The effects of different mixed ratios, confining pressure, frequency on the backbone curves, dynamic shear modulus, and damping ratio characteristics of lightweight soil were investigated. The maximum dynamic shear modulus formula and maximum damping ratio formula of lightweight soil were established. The results indicated that backbone curves of lightweight soil had typical nonlinear and strain-hardening characteristics. With an increase in dynamic shear strain, the dynamic shear modulus curves appeared as inverse “S-shaped”. The dynamic shear modulus in the initial stage varied under different confining pressures. For γd＞10−4, the attenuation rate of the dynamic shear modulus increased with an increase in the dynamic shear strain, and for γd＞10−2, the relationship curve between the dynamic shear modulus and dynamic shear strain coincided gradually, and the attenuation process then stabilized. Owing to the special structure of lightweight soil, the damping ratio curves were “Bell-shaped” and “S-shaped.” Both the confining pressure and compressive yield stress determined the change trends of the damping ratio curves of the lightweight soil. The specimens were in an over-consolidated state and the mixed soil was in the state of shear dilatancy under dynamic loads when the confining pressure was less than the compressive yield stress, resulting in “Bell-shaped” damping ratio curve. The specimens were in the normal consolidated state and the mixed soil was in the state of shear shrinkage under dynamic loads when the confining pressure was greater than the compressive yield stress, resulting in “S-shaped” damping ratio curves. The calculation methods of the relative structural degree k and generalized void ratio e' of lightweight soil were defined to express the structural damage laws of lightweight soil having different mixed ratios under complex stress conditions using a unified formula. Based on Hardin's empirical formulas, the maximum dynamic shear modulus and maximum damping ratio formulas of lightweight soil were established by introducing the relative structural degree k and generalized void ratio e'. The formulas can quantitatively describe the dynamic responses of the special structure of lightweight soil under complex stress conditions.

Short-Long Term Load Prediction Based on GM-BP Model

With the continuous development of the smart grid, rational power planning can greatly increase economic benefits. The power load is affected by temperature, humidity, and other factors, which presents typical nonlinear and random characteristics. Therefore, a combined prediction model based on grey model GM (1, N) and BP neural networks is proposed, where GM is improved by weighting the original data, selecting appropriate initial conditions and self-adaptive optimizing model parameters, while PSO algorithm is used to automatically optimize the global optimization. Finally, an experiment is carried out based on the actual power load data of a certain area, and the results show that the proposed method realizes higher accuracy in load prediction, and has a stronger generalization ability for small samples and low SNR conditions.

A fully coupled hydraulic-mechanical model of deep tunnel considering permeability variation

When the buried depth of underground engineering exceeds 1000 m, the deep surrounding rock will show typical nonlinear deformation characteristics due to the coupling effect of high in-situ stress, and high seepage pressure. The stress field of surrounding rock will change the distribution characteristics of seepage field, and in turn, the change of seepage field will affect the distribution of surrounding rock stress field, which is a process of mutual influence and interaction. Considering the variation of permeability coefficient of deep surrounding rock and non-Darcy flow movement of fluid, a fully coupled hydraulic-mechanical analytical model of deep tunnel was proposed based on Hoek-Brown criterion and effective stress principle, and the detailed application method of the model was also given. Through the triaxial compression test under hydro-mechanical coupling, the nonlinear softening model considering seepage pressure and permeability variation equation were established, which solved two key problems of the model. The corresponding numerical calculation program was compiled based on ABAQUS finite element platform, and the reliability of the calculation program was verified by comparing with the model test results. The influence of the thickness of the grouting layer, the permeability coefficient of the grouting layer, the number of drainage holes and the depth of drainage holes on tunnel surrounding rock seepage pressure and lining seepage pressure were calculated and analyzed, and the anti-seepage optimization design of Xianglushan tunnel was carried out.

Non-linear feature analysis of public emotion evolution for online teaching during the COVID-19 pandemic

PurposeNowadays, the breakout of the COVID-19 pandemic has caused an important change in teaching models. The emotional experience of this change has an important impact on online teaching. This paper aims to explore its time evolution characteristics and provide reference for the development of online teaching in the post epidemic era.Design/methodology/approachThe article firstly crawls the online teaching-related comment text data on Zhihu platform and performs emotional calculation to obtain a one-dimensional time series of daily average emotional values. Then, by using non-linear time-series analysis, this paper reconstructs the daily average emotion value time series in high-dimensional phase space, calculates the maximum Lyapunov exponent and correlation dimension and finally, explores the feature patterns through recurrence plot and recurrence quantification analysis.FindingsIt was found that the sequence has typical non-linear chaotic characteristics; its correlation dimension indicates that it contains obvious fractal characteristics; the public emotional evolution shows a cyclical rise and fall. By text mining and temporal evolution analysis, this paper explores the evolution law over chronically of the daily average emotion value time series, provides feasible strategies to improve students' online learning experience and quality and continuously optimizes this new teaching model in the era of pandemic.Originality/valueBased on social knowledge sharing platform of Q&A, this paper models and analyzes users interaction data under online teaching-related topics. This paper explores the evolution law over a long time period of the daily average emotion value time series using text mining and temporal evolution analysis. It then offers workable solutions to enhance the quality and experience of students' online learning, and it continuously improves this new teaching model in the age of pandemics.

Mathematical Modeling and Optimal Control of Underground Ventilation and Air Conditioning in Station

The underground ventilation and air-conditioning system of the station accounts for about 40% of the total energy consumption of the subway. One of the important reasons is the cooling load of the station. The related control process has typical nonlinear, time-varying, and large inertia characteristics, and the traditional PID control algorithm is difficult to achieve the ideal control effect. Therefore, it is of great significance to study the application of the intelligent algorithm in subway ventilation and air-conditioning control systems to improve the control effect. Based on the analysis of the coupling characteristics of temperature and humidity control in subway air-conditioning systems, this paper designs the feedforward decoupling link according to the identification model and analyzes the control curves with and without the decoupling link when using conventional PID control, which shows the effectiveness of the method proposed in this paper.

Fault diagnosis of rolling bearing based on multi-scale one-dimensional convolutional neural network

Aiming at the typical non-stationary and nonlinear characteristics of rolling bearing vibration signals, a multi-scale convolutional neural network method for bearing fault diagnosis based on wavelet transform and one-dimensional convolutional neural network is proposed. First, the signal is decomposed into multi scale components with wavelet transform, and then each scale component is reconstructed. The reconstructed signal is subjected to the Fourier transform to obtain the frequency spectrum representation, which is used as the input of the one-dimensional convolutional neural network. Finally, one-dimensional convolution neural network is used to learn the features of the input data and recognize the bearing fault. The performance of the model is verified by using data sets of rolling bearing. The results show that this method can intelligent feature extraction and obtain 99.94% diagnostic accuracy.

Electro-Hydraulic Proportional Position Control Using Auto Disturbance Rejection Based on RBF Neural Network

Large friction force and large dead zone are two typical nonlinear characteristics of electro-hydraulic proportional valve controlled hydraulic cylinder position control system. Aiming at those characteristics, a dead zone dynamic compensation algorithm is researched in order to reduce the lag time and control error. At the same time, a control strategy of radial basis function (RBF) neural network combined with auto disturbance rejection control (ADRC) is researched according to the impact of different conditions. The experimental result shows that the proposed algorithm improves performance of the electro-hydraulic proportional valve controlled hydraulic cylinder position control system. In positioning control experiment, the overshoot is 0 and the stability error is 0. In tracking control experiment, the lag time is reduced from the original 1.5 s to 0.2 s with no flat top phenomenon and the maximum error was reduced from 20 mm to 3 mm.

Identification of Accounting Fraud Based on Support Vector Machine and Logistic Regression Model

The authenticity of the company’s accounting information is an important guarantee for the effective operation of the capital market. Accounting fraud is the tampering and distortion of the company’s public disclosure information. The continuous outbreak of fraud cases has dealt a heavy blow to the confidence of investors, shaken the credit foundation of the capital market, and hindered the healthy and stable development of the capital market. Therefore, it is of great theoretical and practical significance to carry out the research on the identification and governance of accounting fraud. Traditionally, accounting fraud identification is mostly based on linear thinking to build the fraud identification model. However, more and more studies show that fraud has typical nonlinear characteristics, and the multiobjective of fraud means also determines the limitations of using the linear model for identification. Considering that the traditional identification methods may have the defects of model setting error and insufficient information extraction, this paper constructs the support vector machine and logistic regression model to identify accounting fraud. The support vector machine is used to improve the learning ability and generalization ability of unknown phenomena, and the explanatory power of each variable to the whole model is identified by the logistic regression model. This paper breaks through the linear constraint hypothesis and explores the model setting form which is more suitable for the law of corporate fraud behaviour to extract the fraud identification information more fully and provide more powerful support for investors to effectively identify fraud.

Damage Identification of Semi-Rigid Connection in Structures Based on Nonlinear Vibration Characteristics

A damage-detection methodology based on nonlinear vibration characteristics was presented in this paper to identify damage of semi-rigid connections due to looseness of high strength bolts. In various damage cases caused by different loose severity of bolts in different positions, a specimen with semi-rigid connections was test and acceleration signals of free vibration were recorded by sensors placed at its both ends. The recorded signals were then dealt with by wavelet de-noising and blind source separation to get the first vibrational components. By Hilbert transform of these components, the amplitude-frequency curve of each case was obtained. The results showed that vibration of the specimen with semi-rigid connections presented typical nonlinear characteristics. With the increase of connection damage, the nonlinear degree increased accordingly. When one end had larger connection damage than the other one, it exhibited greater nonlinear extent. The results prove that it is valuable to apply the suggested nonlinear vibration characteristics based procedure to identification of semi-rigid connection damage.