1,256 publications from this institution
In this paper we present the application of regressive models to simulation of a full-scale vehicle-to-pole impact. The capability of an ARMAX model to reproduce vehicle kinematics was examined. Regressive model parameters were established by minimizing a weighted sum of squares of prediction errors. The prediction horizon was assigned to evaluate model's robustness and verify its time series data forecasting performance. It was found that the ARMAX model reproduces the signal which was used for its establishment (i.e. real vehicle's acceleration). Moreover, such estimation technique preserves all characteristic information relevant for a given collision, since integration of the estimated acceleration pulse yields plots of velocity and displacement which closely follow the reference ones.
This paper investigates the electrically operated pitch systems of offshore wind turbines for online condition monitoring and health assessment. The current signature based fault diagnostics is developed for electrically operated pitch systems using model-based approach. The electrical motor faults are firstly modelled based on modified winding function theory and then, current signature analysis is performed to detect the faults. Further, in order to verify the fault diagnostics capabilities in realistic conditions, the operating profiles are obtained from FAST simulation of offshore wind turbines in various wind conditions. In this way, the applicability of current signature analysis for fault diagnostics in offshore wind turbine pitch systems is demonstrated.
Snake-like robots can imitate the movement patterns of animals in nature and enter the space that traditional robots cannot enter, which adapt to environments that humans cannot reach, and expand the field of human exploration. However, it is often challenging to realize autonomous navigation and simultaneously avoid obstacles under an unknown environment, that is, active SLAM (Simultaneous Localization and Mapping). This paper proposes an autonomous obstacle avoidance method combined with SLAM based on deep reinforcement learning for a wheeled snake robot by using a multi-sensor. Firstly, we design a modular wheeled snake robot structure with lightweight materials based on orthogonal joints and build a three-dimensional model of a snake robot in Gazebo. Secondly, the SLAM based on two-dimensional LiDAR and IMU is used to realize autonomous navigation under an unknown environment and detect obstacles. At the same time, a Deep Q-Learning-based path planning method of the snake robot is proposed to realize obstacles avoidance during navigation. Finally, simulation studies and experiments show that the designed snake-like robot can realize effective path planning and environmental mapping in environments with obstacles. The proposed active SLAM algorithm improves the success rate of snake-like robot path planning, has better obstacle avoidance ability for obstacles, and reduces the number of collisions compared with the traditional A* and the sampling-based RRT* algorithms.
In this paper, a control strategy to reduce the vibrational response of adjacent buildings under seismic excitation is presented. The proposed strategy combines passive linking elements with an active decentralized control system. The overall active-passive control system admits decentralized design and operation, and achieves an excellent vibrational reduction when the active control system works; in case of a full or partial failure of the active control system, a remarkable reduction in the vibrational response is guaranteed by the passive linking elements. For adjacent buildings that require different levels of seismic protection, the implementation of an active H ∞ control system in just one of the buildings is also considered. The main ideas are presented by means of a simplified two-building model. Numerical simulations have been carried out to assess the performance of the proposed methodology with promising results.
No abstract is provided for this article.
This study investigates the output tracking problem for a class of Takagi–Sugeno fuzzy systems subject to periodic signals and actuator saturation via equivalent-input-disturbance (EID) technique. In particular, to ensure the periodic signals tracking, a state-space repetitive control structure is considered. Further, the EID technique is utilised to improve the disturbance rejection performance without any prior knowledge of the disturbance and inverse dynamics of the plant. By constructing a suitable Lyapunov–Krasovskii functional and using the Wirtinger-based integral inequality, a new set of sufficient conditions is derived in terms of linear matrix inequalities (LMIs) which ensures the stability of the addressed system. In addition to that by using a Lyapunov level set, saturation-dependent Lyapunov function captures the real-time information on the severity of actuator saturation and leads to less conservative estimate of the domain of attraction, which is based on the solution of an LMI optimisation problem. Moreover, the designed fuzzy repetitive controller is reliable in the sense that the stability and the satisfactory performance of the closed-loop system are achieved not only under normal operation, but also in the presence of any actuator faults, saturation and dead zone. Finally, the proposed method is validated through two numerical examples to illustrate the effectiveness and superiority of the developed controller design.
This study presents a thorough examination of various Generative Pretrained Transformer (GPT) methodologies in sentiment analysis, specifically in the context of Task 4 on the SemEval 2017 dataset. Three primary strategies are employed: 1) prompt engineering using the advanced GPT-3.5 Turbo, 2) fine-tuning GPT models, and 3) an inventive approach to embedding classification. The research yields detailed comparative insights among these strategies and individual GPT models, revealing their unique strengths and potential limitations. Additionally, the study compares these GPT-based methodologies with other current, high-performing models previously used with the same dataset. The results illustrate the significant superiority of the GPT approaches in terms of predictive performance, more than 22\% in F1-score compared to the state-of-the-art. Further, the paper sheds light on common challenges in sentiment analysis tasks, such as understanding context and detecting sarcasm. It underscores the enhanced capabilities of the GPT models to effectively handle these complexities. Taken together, these findings highlight the promising potential of GPT models in sentiment analysis, setting the stage for future research in this field. The code can be found at https://github.com/DSAatUSU/SentimentGPT
Water heating applications consume a considerable portion of electricity demand in most of countries. Small wind turbines are one of attractive alternatives for grid electricity based water heating systems. Wind energy can be converted to heat energy in a high efficient manner. However it is essential that wind turbine based water heating systems should be economical and reliable. Maximum power point tracking algorithm of most of available wind turbines requires information from a wind speed sensor and a rotor speed sensor which reduces the reliability of the system. In this paper, the proposed 5 kW wind turbine does not require external wind speed sensors and rotor speed sensors. The system is consistent with sensorless maximum power point tracking algorithm, which eliminates the need for both wind speed and rotor speed sensors and gives a highly reliable solution for water heating applications. Internal voltage and current sensors are used to measure the output voltage, the output current and the power of the generator. Using those measurements, the sliding-mode observer can accurately estimate the rotor speed and position and which is used in the maximum power point tracking algorithm. To calculate the optimum rotor speed, the generator output power measurements are used with power signal feedback method. The effectiveness of the proposed system is verified using a simulation model by comparing the performances with a sensor based model.
A policy-iteration-based algorithm is presented in this article for optimal control of unknown continuous-time nonlinear systems subject to bounded inputs by utilizing the adaptive dynamic programming (ADP). Three neural networks (NNs), called critic network, actor network, and quasi-model network, are utilized in the proposed algorithm to give approximations of the control law, the cost function, and the function constituted by partial derivatives of value functions with respect to states and unknown input gain dynamics, respectively. At each iteration, based on the least sum of squares method, the parameters of critic and quasi-model networks will be tuned simultaneously, which eliminates the necessity of separately learning the system model in advance. Then, the control law is improved by satisfying the necessary optimality condition. Then, the proposed algorithm's optimality and convergence properties are exhibited. Finally, the simulation results demonstrate the availability of the proposed algorithm.
No abstract is provided for this article.
This paper is devoted to study the Razumikhin stability theorem for a class of impulsive stochastic delay differential systems. By developing a new lemma, stochastic analysis technique, and Razumikhin approach, several novel criteria of the pth moment exponential stability are derived for the related systems. The key feature of the criteria is that time-derivatives of the Razumikhin functions are allowed to be indefinite, which loosens the constraints of the existing results greatly. Finally, two examples are given to illustrate the usefulness and significance of the theoretical results.
No abstract is provided for this article.
Superresolution array processing, and in particular signal subspace-type techniques, are based effectively on the searching of the array manifold in order to estimate signal parameters. Although the array manifold concept has been used extensively to solve parameter estimation problems, only very recently has research been directed toward identifying the shape of the manifold. Knowledge of the manifold shape is not only essential for the investigation of ambiguities and assessment of the detection-resolution capabilities of an array, but it may also prove useful in developing new and more effective methods for its search process. Concepts related to two array manifold properties, namely arc length and first curvature, are used to assess the accuracy and resolution capabilities offered by a general array in the presence of near-field emitters.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
No abstract is provided for this article.
