Simulation Experiments Research Articles

Posterior Representations for Bayesian Context Trees: Sampling, Estimation and Convergence

We revisit the Bayesian Context Trees (BCT) modelling framework for discrete time series, which was recently found to be very effective in numerous tasks including model selection, estimation and prediction. A novel representation of the induced posterior distribution on model space is derived in terms of a simple branching process, and several consequences of this are explored in theory and in practice. First, it is shown that the branching process representation leads to a simple variable-dimensional Monte Carlo sampler for the joint posterior distribution on models and parameters, which can efficiently produce independent samples. This sampler is found to be more efficient than earlier MCMC samplers for the same tasks. Then, the branching process representation is used to establish the asymptotic consistency of the BCT posterior, including the derivation of an almost-sure convergence rate. Finally, an extensive study is carried out on the performance of the induced Bayesian entropy estimator. Its utility is illustrated through both simulation experiments and real-world applications, where it is found to outperform several state-of-the-art methods.

A facile and scalable fabrication procedure for PVDF-PDA/PEI/SiO2 hollow fiber composite ultrafiltration membranes: Integration of co-deposition and cross-linking

Polyvinylidene fluoride (PVDF) membranes, widely used for their stability, face limitations due to the hardly adjustable pore size in nanoscale and inherent hydrophobicity. To address this, we proposed a co-deposition method incorporating dopamine (DA), polyethyleneimine (PEI), and silicon dioxide (SiO2) nanoparticles with glutaraldehyde cross-linking for precise regulation of pore size and hydrophilicity in PVDF hollow fiber membranes. Notably, the pore diameter reduced from 119.0 ± 3.55 nm to 18.4 ± 0.36 nm, while the contact angle decreased from 107.7° to 40.9°. This simultaneous regulation led to a high-performance PDA/PEI/SiO2-PVDF hydrophilic ultrafiltration membrane with a rejection rate exceeding 99% for 20 nm nanoparticles in a long-term simulated separation experiment while achieving a significant protein permeability of 97% in BSA permeation experiments. Additionally, the membrane demonstrated exceptional long-term stability, offering a novel approach for tuning the performance of ultrafiltration/microfiltration membranes.

Comprehensive performance evaluation of basic type of lunar habitat

ABSTRACT Amid the burgeoning lunar economy, habitat establishment is vital for exploration. The investigation into lunar habitat morphology requires systematic organization. To systematically and comprehensively delve into lunar habitats morphological design, this study analyzes spatial elements and relationships through the profiling of lunar habitats. It comprehensively analyzes spatial elements and relationships, laying the groundwork for a dedicated performance evaluation system. It provides theoretical and technical references for lunar habitat forms in the early design phase. First, it summarizes four basic types of lunar habitat profiles. Second, it selects 23 factors influencing lunar habitat profile design, using the Delphi method to filter six major ones. Third, various simulation experiments assess each type’s performance under factors. Fourth, the TOPSIS method analyzes comprehensive performance. This study has developed a comprehensive performance evaluation system and an integrated experimental methodology for assessing the architectural profile morphology of lunar habitat structures. Employing the evaluation system, a comprehensive experimental analysis was conducted on four basic types in the current phase. This analysis yielded two design suggestions for profile elements and three optimization directions for profile design. The discussion offers insights for future evaluations, considering mission innovation and technological advancements.

An Adaptive Discrete Integral Terminal Sliding Mode Control Method for a Two-Joint Manipulator

In response to the trajectory tracking control problem of manipulators under measurement disturbances, a novel multi-input multi-output discrete integral terminal sliding mode control scheme is proposed. Initially, this scheme establishes a dynamic model of a two-joint manipulator based on the Lagrangian dynamics analysis method. Subsequently, a discrete integral terminal sliding mode control law based on the dynamic model of the two joints is designed, incorporating delayed estimation of unknown disturbances and discretization errors in the manipulator system. To enhance the trajectory tracking accuracy of the control scheme and suppress the impact of sliding mode chattering on the manipulator system, an adaptive switching term is introduced into the discrete integral terminal sliding mode control law. The paper derives an adaptive discrete integral terminal sliding mode control scheme and provides stability proof for the proposed approach. Simulation experiments are conducted to compare the proposed adaptive discrete integral terminal sliding mode control scheme with classical discrete sliding mode control schemes and discrete integral terminal sliding mode control schemes. The simulation results demonstrate that the designed adaptive discrete integral terminal sliding mode control scheme maintains trajectory tracking errors within 0.004 radians for each joint of the manipulator, with minimal changes in control torque for each joint. The absolute integral of the control torque variations is calculated at 5.85×103, which is lower than other control schemes, thereby validating the effectiveness and superiority of the proposed approach.

Active Vibration Avoidance Method for Variable Speed Welding in Robotic Friction Stir Welding Based on Constant Heat Input

Robotic Friction Stir Welding (RFSW) technology integrates the advantages of friction stir welding and industrial robots, finding extensive applications and research in aerospace, shipbuilding, and new energy vehicles. However, the high-speed rotational process of friction stir welding combined with the low stiffness characteristics of serial industrial robots inevitably introduces vibrations during the welding process. This paper investigates the vibration patterns and impacts during the RFSW process and proposes an active vibration avoidance control method for variable speed welding based on constant heat input. This method utilizes a vibration feedback strategy that adjusts the spindle speed actively if the end-effector’s vibration exceeds a threshold, thereby avoiding the modal frequencies of the robot at its current pose. Concurrently, it calculates and adjusts the welding speed of the robot according to the thermal equilibrium equation to maintain constant heat input. A simplified dynamic model of the RFSW robot was established, and the feasibility of this method was validated through simulation experiments. This study fills the gap in vibration analysis of RFSW and provides new insights into control strategies and process optimization for robotic friction stir welding.

A Novel Error Modulation Rotation Scheme for Dual-Axis Rotational Inertial Navigation System

Abstract Dual-axis rotational inertial navigation system (RINS) can effectively suppress the error accumulation of inertial navigation system, thereby improve the long-term navigation accuracy through rotating inertial measurement unit (IMU) according to corresponding rotation scheme. The navigation accuracy of RINS is predominantly contingent on the IMU rotation scheme. A reasonable rotation scheme can effectively attenuate or even obviate the navigation errors stemming from IMU errors, whereas an ill-considered scheme may lead to swift divergence of navigation errors. In this paper, the principle of IMU errors suppression is meticulously analyzed during rotation. By comprehensively evaluating the impact of individual IMU errors on navigation accuracy alongside their corresponding error modulation characteristics, a novel 32-position rotation scheme is devised. The proposed scheme demonstrates a notable capacity to minimize the adverse effects of IMU errors on navigation accuracy and to enhance error suppression efficacy within a modulation cycle. Meanwhile, the long-endurance navigation accuracy of RINS is substantially enhanced. The efficacy of the 32-position scheme posited herein is empirically substantiated through a 24-hour simulation and navigation experiment.

Analysis of heating defects in extension rods of extra-high voltage direct current overhead transmission lines

This paper studies the overheating defect of an extension rod in a ± 800 kV extra-high voltage direct current overhead transmission line, and proposes various factors that affect the overheating of the extension rod. Establish an equivalent model of an extra high voltage DC transmission line based on equipotential point and line parameter analysis, and calculate the model parameters. The proposed model can also be applied to ultra-high voltage DC overhead transmission lines such as ± 500 kV, and has strong universality. Simulation experiments show that if the equivalent resistance value of the split wire side is reduced by 0.01Ω, the power loss on the extension tie rod can be increased to 380.951 W; if the equivalent resistance value of the jumper side is increased by 0.01Ω, the power loss on the extension tie rod can be increased to 273.41 W. Combined with theoretical analysis and simulation verification, it is concluded that the main factor affecting the overheating of the extension rod is the change in the resistance value on the jumper side caused by the increase in contact resistance. A systematic troubleshooting plan for related defect problems is proposed, which has important reference and guidance significance for such line fault problems.

A cooperative control method and application for series multivariable coupled system

Series multivariable coupled system is a typical controlled object in process control industry. The interaction of various state variables between multiple inputs and outputs in the system forms a complex series multivariable coupled structure. This coupled structure makes the control of a controlled object in the system affect the controlled object in the upper and lower control loop. As a result, it is difficult to control one or more control loops in the system without changing the state of other links in the system. In this paper, a cooperative control method for series multivariable coupled system is proposed. A decoupling controller is designed to remove the coupling effect caused by the interaction between stages, and the system is decoupled into several independent control loops. Differential leading PI (proportional-integral) error compensation method is introduced to ensure the following performance of the controller without static error. The proposed cooperative control method satisfies the Lyapunov stability, and has been successfully applied in the simulation experiment of cascade pumping station system of Beijing East-to-West water transfer project. The proposed method reduces the difficulty to controlling the water level of forebay of each pumping station and ensures the efficient operation of the cascade pumping station system.

Trajectory Tracking Control of Unmanned Underwater Vehicle Based on Projected Perpendicular Guidance Method with Disturbance Observer

Unmanned underwater vehicles (UUVs) possess impressive maneuverability and versatility, but controlling them during trajectory tracking can be challenging due to their susceptibility to external disturbances and perturbations in their model parameters. Additionally, the UUV has four degrees of freedom underwater, but only three control inputs, making it a typical underactuated system. To address these issues, this paper introduces a novel optimize sliding mode control (OPSMC) algorithm grounded in projected perpendicular guidance (PPG). The PPG algorithm transforms the three-degree-of-freedom path trajectory control into two-degree-of-freedom heading tracking control and surge velocity tracking control by designing virtual posture angles. Optimized sliding mode control, based on sliding mode control, improves control precision and reduces control input chattering by constructing optimization functions for control inputs. During trajectory tracking, UUVs are susceptible to external environmental disturbances and perturbations in system model parameters. Disturbance observers are employed to estimate these disturbances and perturbations. Finally, MATLAB/Simulink is used for numerical simulation experiments. The simulation results demonstrate that the PPG algorithm effectively enables underactuated UUVs to achieve trajectory tracking control. The designed optimized sliding mode controller and disturbance observer enhance the control precision and robustness of the system.

Study on the Emission of Connected Autonomous Vehicle Considering the Control of Electronic Throttle Opening

Aiming at the networked cruise control scenario of CAV (connected autonomous vehicle) queue, we propose a new networked cruise control strategy for CAV by introducing the average information of ET (electronic throttle) opening of the downstream vehicle group as a feedback signal. By performing linear stability analysis on the new model, we derive its linear stability conditions. Further, we design exhaustive numerical simulation experiments aiming to systematically investigate the effect of the multi-vehicle ahead electronic throttle opening average feedback signal on CAV traffic stability, fuel consumption, and key emission factors, such as CO, HC, and NOx, during the cruise control process. The results show that the feedback signal can not only significantly improve the operational stability of the CAV traffic flow but also significantly improve its fuel consumption and the emission levels of CO, HC, and NOx. When the number of CAV vehicles in the feedback signal is set to three, the levels of CO, HC, and NOx emissions as well as fuel consumption in the road system can reach a stable and optimized state.

Learning Advanced Locomotion for Quadrupedal Robots: A Distributed Multi-Agent Reinforcement Learning Framework with Riemannian Motion Policies

Recent advancements in quadrupedal robotics have explored the motor potential of these machines beyond simple walking, enabling highly dynamic skills such as jumping, backflips, and even bipedal locomotion. While reinforcement learning has demonstrated excellent performance in this domain, it often relies on complex reward function tuning and prolonged training times, and the interpretability is not satisfactory. Riemannian motion policies, a reactive control method, excel in handling highly dynamic systems but are generally limited to fully actuated systems, making their application to underactuated quadrupedal robots challenging. To address these limitations, we propose a novel framework that treats each leg of a quadrupedal robot as an intelligent agent and employs multi-agent reinforcement learning to coordinate the motion of all four legs. This decomposition satisfies the conditions for utilizing Riemannian motion policies and eliminates the need for complex reward functions, simplifying the learning process for high-level motion modalities. Our simulation experiments demonstrate that the proposed method enables quadrupedal robots to learn stable locomotion using three, two, or even a single leg, offering advantages in training speed, success rate, and stability compared to traditional approaches, and better interpretability. This research explores the possibility of developing more efficient and adaptable control policies for quadrupedal robots.

A Compensation Method for the Geomagnetic Measurement Error of an Underwater Ship-Borne Magnetometer Based on Constrained Total Least Squares

When magnetic matching aided navigation is applied to an underwater vehicle, the magnetometer must be installed inside the vehicle, considering the navigation safety and concealment of the underwater vehicle. Then, the interference magnetic field will seriously affect the accuracy of geomagnetic field measurement, which directly affects the accuracy of geomagnetic matching aided navigation. Therefore, improving the accuracy of geomagnetic measurements inside the vehicle through error compensation has become one of the most difficult problems that requires an urgent solution in geomagnetic matching aided navigation. In order to solve this problem, this paper establishes the calculation model of the internal magnetic field of the underwater vehicle and the geomagnetic measurement error model of the ship-borne magnetometer. Then, a compensation method for the geomagnetic measurement error of the ship-borne magnetometer, based on the constrained total least square method, is proposed. To verify the effectiveness of the method proposed in this paper, a simulation experiment of geomagnetic measurement and compensation of a ship-borne three-axis magnetometer was constructed. Among them, to be closer to the real situation, a combination of the geomagnetism model, the elliptic shell model and the magnetic dipole model was used to simulate the internal magnetic field of the underwater vehicle. The experimental results indicated that the root mean square error of geomagnetic measurement in an underwater vehicle was less than 5 nT after compensation, and the accuracy of geomagnetic measurement met the requirements of geomagnetic matching aided navigation.

Efficient estimation of true fixed-effects stochastic frontier models

AbstractFixed-effects modeling has become the method of choice in several panel data settings, including models for stochastic frontier analysis. A notable instance of stochastic frontier panel data models is the true fixed-effects model, which allows disentangling unit heterogeneity from efficiency evaluations. While such a model is theoretically appealing, its estimation is hampered by incidental parameters. This note proposes a simple and rather general estimation approach where the unit-specific intercepts are integrated out of the likelihood function. We apply the theory of composite group families to the model of interest and demonstrate that the resulting integrated likelihood is a marginal likelihood with desirable inferential properties. The derivation of the result is provided in full, along with some connections with the existing literature and computational details. The method is illustrated for three notable models, given by the normal-half normal model, the heteroscedastic exponential model, and the normal-gamma model. The results of simulation experiments highlight the properties of the methodology.

Fault-tolerant joint state feedback adaptive control for snake robots that move through random pole environments

ABSTRACT In unstructured terrain, snakes can move more efficiently by actively pushing and avoiding obstacles, a capability believed to be realized through a decentralized mechanism. Moreover, considering harsh and unpredictable rescue environments where snake robots are expected to be deployed, such as post-earthquake scenarios, some joint sensors are prone to failure. We propose a decentralized adaptation mechanism for snake robots to navigate through random pole environments. This mechanism integrates Fault-Tolerant Joint State Feedback Adaptive Control (FT-JSFAC) with Extended Curvature Derivatives control (ECD) and Angular Damping control (AD), leveraging the FT-JSFAC method to maintain operational robustness even when some joint sensors fail. To meet critical goals of adaptability, we utilize real-time angle and electrical current data from the robot's internal servo motors. Using this data, FT-JSFAC determines whether an obstacle is beneficial or detrimental to propulsion and then allows for decentralized torque adjustment to adapt to the environment. FT-JSFAC also employs historical data from functioning joints to compensate for erroneous sensor data, ensuring operational robustness even with joint sensor failure. Simulation experiments demonstrate that the proposed mechanism effectively adapts to environmental obstacles and maintains adaptability when one or two joint sensors fail.

A Stackelberg game-based on-ramp merging controller for connected automated vehicles in mixed traffic flow

ABSTRACT This paper proposes a game theory-based on-ramp merging controller for connected automated vehicles (CAVs) in mixed traffic flow. First, a two-layer decision-making framework based on the Stackelberg game is designed to consider the fuel consumption and safety payoffs of mixed traffic flow under different driving behaviors. The upper layer of the framework determines the optimal merging decision (i.e. merging time and location) for on-ramp vehicles (RVs) based on the Stackelberg game. The lower layer optimizes the merging trajectory of CAVs to reduce energy consumption and safety risks during the ramp-merging process. Then, a driving behavior estimation algorithm is developed to describe the differences in mainline vehicles (MLVs) response to the merging behavior of RVs. Finally, the simulation experiments are adopted to verify the effectiveness and stability of the proposed framework. The results indicated that, the proposed framework promotes environmental protection, operational efficiency, and traffic flow stability in different traffic scenarios.

Robustness of consensus in double‐layer degree positive and negative correlation ring networks

The robustness of consensus in multilayer networks is affected by the network structure, especially the interlayer coupling approach. The article investigates the impact of two interlayer coupling approaches on the robustness of consensus in double‐layer ring networks, where the interlayer relationships are denoted as degree positive correlation (PC) and degree negative correlation (NC), respectively. The robustness of network consensus can be derived from network coherence. Two types of network coherence are first described using the Kirchhoff index and the number of spanning trees, providing a new calculation method for network coherence. The number of network nodes and the coupling strength have been shown to impact the coherence of the two types of networks. Combined with numerical simulation experiments, the NC model shows better consensus than the PC model under the same parameter set. In particular, the parameter values when the network consensus is optimal are obtained.

Formation Control of Autonomous Underwater Vehicles Using an Improved Nonlinear Backstepping Method

The characteristics of autonomous underwater vehicles include nonlinearity, strong coupling, multiple inputs and multiple outputs, uncertainty, strong disturbance, underdrive, and multiple constraints. Autonomous underwater vehicle cluster systems are associated with large-scale complex dynamic systems through local perception or network communication, which have the structural characteristics of “complex dynamic + association topology + interaction rules”. To solve the problem of formation trajectory tracking of underactuated autonomous underwater vehicles, a controller was designed on the basis of an improved nonlinear backstepping algorithm, cascade system theory, and the Lyapunov direct method. In this design, the formation is determined from the actual trajectory of the leader autonomous underwater vehicle. The formation control rate is determined using the backstepping method and Lyapunov theory. Nonlinear disturbance observers were added to ensure that the trajectory error of the formation control could be quickly reduced in a real case with interference. The stability and effectiveness of this method were verified through simulation experiments. The robustness of the control algorithm was verified using two simulation cases, and the simulation results show that the proposed control method can maintain the expected formation.

Application of an Improved Convolutional Neural Network Algorithm in Text Classification

This paper proposes a text classification model based on a combination of a convolutional neural network (CNN) and a support vector machine (SVM) using Amazon review polarity, TREC, and Kaggle as experimental data. By adding an attention mechanism to simplify the parameters and using the classifier based on SVM to replace the Softmax layer, the extraction effect of feature words is improved and the problem of weak generalization ability of the CNN model is solved. Simulation experiments show that the proposed algorithm performs better in precision rate, recall rate, F1 value, and training time compared with CNN, RNN, BERT and term frequency-inverse document frequency (TF-IDF).

Adaptive Network-Based Fuzzy Inference System–Proportional–Integral–Derivative Controller Based on FPGA and Its Application in Radiofrequency Ablation Temperature Control

The radiofrequency ablation temperature system is characterised by its time-varying, non-linear, and hysteretic nature. The application of PID controllers to the control of radiofrequency ablation temperature systems has a number of challenges, including overshoot, dependence on high-precision mathematical models, and difficulty in parameter tuning. Therefore, in order to improve the effectiveness of radiofrequency ablation temperature control, an adaptive network-based fuzzy inference system combined with an incremental PID controller was used to optimise the shortcomings of the PID controller in radiofrequency ablation temperature control. At the same time, the learning rate at the time of updating the consequence parameters was set by segmentation to solve the problem of poor control accuracy when the ANFIS-PID controller is implemented based on FPGA fixed-point decimals. Based on FPGA-in-the-loop simulation experiments and ex vivo experiments, the effectiveness of the ANFIS-PID controller in the temperature control of radiofrequency ablation was verified and compared with the PID controller under the same conditions. The experimental results show that the ANFIS-PID controller has a superior performance in terms of tracking capability and stability compared with the PID controller.

Multimedia Human-Computer Interaction Method in Video Animation Based on Artificial Intelligence Technology

With the development of computer technology innovation, be able to deal with the media comprehensive information and real-time information interaction with the computer multimedia technology arises at the historic moment, it promotes the application fields of computer widen to industrial all aspects of life. As the product of digital technology, animation technology plays an irreplaceable role in the production of multimedia courseware. However, the existing human-computer interaction methods have shortcomings such as incomplete extraction of video features and poor human-computer interaction effect. In this context, this paper designs a multimedia human-computer interaction method for animation works based on CNN model. First of all, the original video data is collected and preprocessed. Then it is input into the HCI framework based on CNN model for feature extraction. Finally, the effectiveness and practicability of the proposed method are proved by simulation experiments, which provides a reference and basis for the research of modern human-computer interaction.